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40,700 | 40,700 | ['Valentina Zantedeschi', 'Rémi Emonet', 'Marc Sebban'] | 1604.01376v1 | Many theoretical results in the machine learning domain stand only for
functions that are Lipschitz continuous. Lipschitz continuity is a strong form
of continuity that linearly bounds the variations of a function. In this paper,
we derive tight Lipschitz constants for two families of metrics: Mahalanobis
distances and bounded-space bilinear forms. To our knowledge, this is the first
time the Mahalanobis distance is formally proved to be Lipschitz continuous and
that such tight Lipschitz constants are derived. | Lipschitz Continuity of Mahalanobis Distances and Bilinear Forms | 2,016 | http://arxiv.org/pdf/1604.01376v1 | Title Lipschitz Continuity Mahalanobis Distances Bilinear Forms Summary Many theoretical result machine learning domain stand function Lipschitz continuous Lipschitz continuity strong form continuity linearly bound variation function paper derive tight Lipschitz constant two family metric Mahalanobis distance boundedspace bilinear form knowledge first time Mahalanobis distance formally proved Lipschitz continuous tight Lipschitz constant derived Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 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40,701 | 40,701 | ['Mikhail Kolmogorov', 'Eamonn Kennedy', 'Zhuxin Dong', 'Gregory Timp', 'Pavel Pevzner'] | 1604.02270v2 | Recent advances in top-down mass spectrometry enabled identification of
intact proteins, but this technology still faces challenges. For example,
top-down mass spectrometry suffers from a lack of sensitivity since the ion
counts for a single fragmentation event are often low. In contrast, nanopore
technology is exquisitely sensitive to single intact molecules, but it has only
been successfully applied to DNA sequencing, so far. Here, we explore the
potential of sub-nanopores for single-molecule protein identification (SMPI)
and describe an algorithm for identification of the electrical current blockade
signal (nanospectrum) resulting from the translocation of a denaturated,
linearly charged protein through a sub-nanopore. The analysis of identification
p-values suggests that the current technology is already sufficient for
matching nanospectra against small protein databases, e.g., protein
identification in bacterial proteomes. | Single-Molecule Protein Identification by Sub-Nanopore Sensors | 2,016 | http://arxiv.org/pdf/1604.02270v2 | Title SingleMolecule Protein Identification SubNanopore Sensors Summary Recent advance topdown mass spectrometry enabled identification intact protein technology still face challenge example topdown mass spectrometry suffers lack sensitivity since ion count single fragmentation event often low contrast nanopore technology exquisitely sensitive single intact molecule successfully applied DNA sequencing far explore potential subnanopores singlemolecule protein identification SMPI describe algorithm identification electrical current blockade signal nanospectrum resulting translocation denaturated linearly charged protein subnanopore analysis identification pvalues suggests current technology already sufficient matching nanospectra small protein database eg protein identification bacterial proteome Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.04107718542218208, -0.01693800278007984, -0.0010070267599076033, -0.006566056050360203, -0.028861355036497116, 0.014015627093613148, 0.04537901654839516, 0.03637571260333061, 0.05919956788420677, -0.02454831823706627, 0.01972126215696335, 0.010610583238303661, 0.016596872359514236, 0.043599583208560944, 0.036110520362854004, 0.0585198812186718, -0.01624121703207493, 0.027875367552042007, -0.02271321415901184, -0.012158428318798542, 0.01996939815580845, -0.026200786232948303, 0.034015119075775146, 0.018501216545701027, 0.013704763725399971, 0.023292720317840576, 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40,702 | 40,702 | ['Dezhi Fang', 'Duen Horng Chau'] | 1604.03034v1 | To process data that do not fit in RAM, conventional wisdom would suggest
using distributed approaches. However, recent research has demonstrated virtual
memory's strong potential in scaling up graph mining algorithms on a single
machine. We propose to use a similar approach for general machine learning. We
contribute: (1) our latest finding that memory mapping is also a feasible
technique for scaling up general machine learning algorithms like logistic
regression and k-means, when data fits in or exceeds RAM (we tested datasets up
to 190GB); (2) an approach, called M3, that enables existing machine learning
algorithms to work with out-of-core datasets through memory mapping, achieving
a speed that is significantly faster than a 4-instance Spark cluster, and
comparable to an 8-instance cluster. | M3: Scaling Up Machine Learning via Memory Mapping | 2,016 | http://arxiv.org/pdf/1604.03034v1 | Title M3 Scaling Machine Learning via Memory Mapping Summary process data fit RAM conventional wisdom would suggest using distributed approach However recent research demonstrated virtual memory strong potential scaling graph mining algorithm single machine propose use similar approach general machine learning contribute 1 latest finding memory mapping also feasible technique scaling general machine learning algorithm like logistic regression kmeans data fit exceeds RAM tested datasets 190GB 2 approach called M3 enables existing machine learning algorithm work outofcore datasets memory mapping achieving speed significantly faster 4instance Spark cluster comparable 8instance cluster Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.0012309930752962828, -0.01963106170296669, -0.03541968762874603, 0.006173100788146257, 0.010170439258217812, 0.012029695324599743, 0.014827153645455837, 0.006141554564237595, 0.045960232615470886, 0.0124385179951787, 0.011542982421815395, -0.006442982237786055, -0.029768619686365128, 0.07875073701143265, -0.009417451918125153, -0.04748532176017761, 0.024655098095536232, 0.0156329907476902, -0.067506343126297, -0.009006606414914131, 0.017267471179366112, 0.006511971354484558, 0.03870310261845589, -0.045793090015649796, -0.03346281498670578, -0.022678885608911514, -0.022265708073973656, -0.02724713459610939, 0.02138812094926834, -0.0046743955463171005, -0.04309012368321419, -0.06921766698360443, 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40,703 | 40,703 | ['Jamie Morgenstern', 'Tim Roughgarden'] | 1604.03171v1 | We present a general framework for proving polynomial sample complexity
bounds for the problem of learning from samples the best auction in a class of
"simple" auctions. Our framework captures all of the most prominent examples of
"simple" auctions, including anonymous and non-anonymous item and bundle
pricings, with either a single or multiple buyers. The technique we propose is
to break the analysis of auctions into two natural pieces. First, one shows
that the set of allocation rules have large amounts of structure; second,
fixing an allocation on a sample, one shows that the set of auctions agreeing
with this allocation on that sample have revenue functions with low
dimensionality. Our results effectively imply that whenever it's possible to
compute a near-optimal simple auction with a known prior, it is also possible
to compute such an auction with an unknown prior (given a polynomial number of
samples). | Learning Simple Auctions | 2,016 | http://arxiv.org/pdf/1604.03171v1 | Title Learning Simple Auctions Summary present general framework proving polynomial sample complexity bound problem learning sample best auction class simple auction framework capture prominent example simple auction including anonymous nonanonymous item bundle pricing either single multiple buyer technique propose break analysis auction two natural piece First one show set allocation rule large amount structure second fixing allocation sample one show set auction agreeing allocation sample revenue function low dimensionality result effectively imply whenever possible compute nearoptimal simple auction known prior also possible compute auction unknown prior given polynomial number sample Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.015656188130378723, 0.057997871190309525, 0.004479135386645794, 0.004249954596161842, -0.02147083356976509, -0.023067956790328026, 0.012895049527287483, 0.0009872923837974668, -0.0014760055346414447, -0.0330054946243763, 0.030572524294257164, 0.03181769698858261, 0.00037701247492805123, 0.05865255743265152, -0.007472584955394268, -0.07849907875061035, -0.013695579953491688, 0.006323437672108412, 0.017917845398187637, -0.07114841043949127, 0.07639241218566895, -0.0384993739426136, -0.05281410366296768, -0.011060747317969799, 0.02941693738102913, -0.042377691715955734, -0.030602630227804184, -0.04121911898255348, -0.029071249067783356, -0.04788859188556671, 0.019108114764094353, 0.013315551914274693, -0.01953517645597458, 0.02537725120782852, 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40,704 | 40,704 | ['Alireza Hajibagheri', 'Gita Sukthankar', 'Kiran Lakkaraju'] | 1604.03221v1 | The aim of link prediction is to forecast connections that are most likely to
occur in the future, based on examples of previously observed links. A key
insight is that it is useful to explicitly model network dynamics, how
frequently links are created or destroyed when doing link prediction. In this
paper, we introduce a new supervised link prediction framework, RPM (Rate
Prediction Model). In addition to network similarity measures, RPM uses the
predicted rate of link modifications, modeled using time series data; it is
implemented in Spark-ML and trained with the original link distribution, rather
than a small balanced subset. We compare the use of this network dynamics model
to directly creating time series of network similarity measures. Our
experiments show that RPM, which leverages predicted rates, outperforms the use
of network similarity measures, either individually or within a time series. | Leveraging Network Dynamics for Improved Link Prediction | 2,016 | http://arxiv.org/pdf/1604.03221v1 | Title Leveraging Network Dynamics Improved Link Prediction Summary aim link prediction forecast connection likely occur future based example previously observed link key insight useful explicitly model network dynamic frequently link created destroyed link prediction paper introduce new supervised link prediction framework RPM Rate Prediction Model addition network similarity measure RPM us predicted rate link modification modeled using time series data implemented SparkML trained original link distribution rather small balanced subset compare use network dynamic model directly creating time series network similarity measure experiment show RPM leverage predicted rate outperforms use network similarity measure either individually within time series Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.028287021443247795, 0.009154113940894604, -0.055406197905540466, -0.024403385818004608, -0.021003421396017075, -0.060051947832107544, 0.0069195847027003765, -0.027979839593172073, 0.012210655957460403, -0.016551803797483444, 0.04451907053589821, 0.013082948513329029, 0.00047319746227003634, 0.05541427433490753, 0.006219540722668171, -0.03207025304436684, 0.033863384276628494, 0.008312118239700794, -0.054815858602523804, 0.03787659853696823, 0.051914237439632416, 0.01329888217151165, 0.06189041584730148, 0.022071272134780884, -0.019028406590223312, 0.0083999652415514, 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40,705 | 40,705 | ['Mallory Sheth', 'Roy Welsch', 'Natasha Markuzon'] | 1604.03248v1 | In many data classification problems, there is no linear relationship between
an explanatory and the dependent variables. Instead, there may be ranges of the
input variable for which the observed outcome is signficantly more or less
likely. This paper describes an algorithm for automatic detection of such
thresholds, called the Univariate Flagging Algorithm (UFA). The algorithm
searches for a separation that optimizes the difference between separated areas
while providing the maximum support. We evaluate its performance using three
examples and demonstrate that thresholds identified by the algorithm align well
with visual inspection and subject matter expertise. We also introduce two
classification approaches that use UFA and show that the performance attained
on unseen test data is equal to or better than that of more traditional
classifiers. We demonstrate that the proposed algorithm is robust against
missing data and noise, is scalable, and is easy to interpret and visualize. It
is also well suited for problems where incidence of the target is low. | The Univariate Flagging Algorithm (UFA): a Fully-Automated Approach for
Identifying Optimal Thresholds in Data | 2,016 | http://arxiv.org/pdf/1604.03248v1 | Title Univariate Flagging Algorithm UFA FullyAutomated Approach Identifying Optimal Thresholds Data Summary many data classification problem linear relationship explanatory dependent variable Instead may range input variable observed outcome signficantly le likely paper describes algorithm automatic detection threshold called Univariate Flagging Algorithm UFA algorithm search separation optimizes difference separated area providing maximum support evaluate performance using three example demonstrate threshold identified algorithm align well visual inspection subject matter expertise also introduce two classification approach use UFA show performance attained unseen test data equal better traditional classifier demonstrate proposed algorithm robust missing data noise scalable easy interpret visualize also well suited problem incidence target low Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.005047440994530916, 0.03361004963517189, -0.02029774896800518, -0.029717547819018364, 0.01463885698467493, 0.011494719423353672, 0.057105161249637604, 0.024525683373212814, -0.012788082472980022, -0.02563946135342121, 0.07162503153085709, 0.05136286839842796, -0.025343304499983788, 0.059940800070762634, -0.08690842986106873, 0.008215340785682201, 0.009126940742135048, 0.01199862640351057, 0.02529146522283554, -0.00259134778752923, 0.01466869842261076, -0.04746263846755028, -0.0031623286195099354, -0.011478799395263195, -0.05756031721830368, -0.0004365192144177854, 0.00932222604751587, 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40,706 | 40,706 | ['Raef Bassily', 'Yoav Freund'] | 1604.03336v2 | In this paper, we introduce a notion of algorithmic stability called typical
stability. When our goal is to release real-valued queries (statistics)
computed over a dataset, this notion does not require the queries to be of
bounded sensitivity -- a condition that is generally assumed under differential
privacy [DMNS06, Dwork06] when used as a notion of algorithmic stability
[DFHPRR15a, DFHPRR15b, BNSSSU16] -- nor does it require the samples in the
dataset to be independent -- a condition that is usually assumed when
generalization-error guarantees are sought. Instead, typical stability requires
the output of the query, when computed on a dataset drawn from the underlying
distribution, to be concentrated around its expected value with respect to that
distribution.
We discuss the implications of typical stability on the generalization error
(i.e., the difference between the value of the query computed on the dataset
and the expected value of the query with respect to the true data
distribution). We show that typical stability can control generalization error
in adaptive data analysis even when the samples in the dataset are not
necessarily independent and when queries to be computed are not necessarily of
bounded-sensitivity as long as the results of the queries over the dataset
(i.e., the computed statistics) follow a distribution with a "light" tail.
Examples of such queries include, but not limited to, subgaussian and
subexponential queries.
We also discuss the composition guarantees of typical stability and prove
composition theorems that characterize the degradation of the parameters of
typical stability under $k$-fold adaptive composition. We also give simple
noise-addition algorithms that achieve this notion. These algorithms are
similar to their differentially private counterparts, however, the added noise
is calibrated differently. | Typical Stability | 2,016 | http://arxiv.org/pdf/1604.03336v2 | Title Typical Stability Summary paper introduce notion algorithmic stability called typical stability goal release realvalued query statistic computed dataset notion require query bounded sensitivity condition generally assumed differential privacy DMNS06 Dwork06 used notion algorithmic stability DFHPRR15a DFHPRR15b BNSSSU16 require sample dataset independent condition usually assumed generalizationerror guarantee sought Instead typical stability requires output query computed dataset drawn underlying distribution concentrated around expected value respect distribution discus implication typical stability generalization error ie difference value query computed dataset expected value query respect true data distribution show typical stability control generalization error adaptive data analysis even sample dataset necessarily independent query computed necessarily boundedsensitivity long result query dataset ie computed statistic follow distribution light tail Examples query include limited subgaussian subexponential query also discus composition guarantee typical stability prove composition theorem characterize degradation parameter typical stability kfold adaptive composition also give simple noiseaddition algorithm achieve notion algorithm similar differentially private counterpart however added noise calibrated differently Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.054249875247478485, 0.016651146113872528, -0.03212488815188408, -0.00514144916087389, 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40,707 | 40,707 | ['Liangjie Hong', 'Adnan Boz'] | 1604.03506v1 | One of missions for personalization systems and recommender systems is to
show content items according to users' personal interests. In order to achieve
such goal, these systems are learning user interests over time and trying to
present content items tailoring to user profiles. Recommending items according
to users' preferences has been investigated extensively in the past few years,
mainly thanks for the popularity of Netflix competition. In a real setting,
users may be attracted by a subset of those items and interact with them, only
leaving partial feedbacks to the system to learn in the next cycle, which leads
to significant biases into systems and hence results in a situation where user
engagement metrics cannot be improved over time. The problem is not just for
one component of the system. The data collected from users is usually used in
many different tasks, including learning ranking functions, building user
profiles and constructing content classifiers. Once the data is biased, all
these downstream use cases would be impacted as well. Therefore, it would be
beneficial to gather unbiased data through user interactions. Traditionally,
unbiased data collection is done through showing items uniformly sampling from
the content pool. However, this simple scheme is not feasible as it risks user
engagement metrics and it takes long time to gather user feedbacks. In this
paper, we introduce a user-friendly unbiased data collection framework, by
utilizing methods developed in the exploitation and exploration literature. We
discuss how the framework is different from normal multi-armed bandit problems
and why such method is needed. We layout a novel Thompson sampling for
Bernoulli ranked-list to effectively balance user experiences and data
collection. The proposed method is validated from a real bucket test and we
show strong results comparing to old algorithms | An Unbiased Data Collection and Content Exploitation/Exploration
Strategy for Personalization | 2,016 | http://arxiv.org/pdf/1604.03506v1 | Title Unbiased Data Collection Content ExploitationExploration Strategy Personalization Summary One mission personalization system recommender system show content item according user personal interest order achieve goal system learning user interest time trying present content item tailoring user profile Recommending item according user preference investigated extensively past year mainly thanks popularity Netflix competition real setting user may attracted subset item interact leaving partial feedback system learn next cycle lead significant bias system hence result situation user engagement metric cannot improved time problem one component system data collected user usually used many different task including learning ranking function building user profile constructing content classifier data biased downstream use case would impacted well Therefore would beneficial gather unbiased data user interaction Traditionally unbiased data collection done showing item uniformly sampling content pool However simple scheme feasible risk user engagement metric take long time gather user feedback paper introduce userfriendly unbiased data collection framework utilizing method developed exploitation exploration literature discus framework different normal multiarmed bandit problem method needed layout novel Thompson sampling Bernoulli rankedlist effectively balance user experience data collection proposed method validated real bucket test show strong result comparing old algorithm Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name 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40,708 | 40,708 | ['Zhouyuan Huo', 'Heng Huang'] | 1604.03584v4 | We provide the first theoretical analysis on the convergence rate of the
asynchronous stochastic variance reduced gradient (SVRG) descent algorithm on
non-convex optimization. Recent studies have shown that the asynchronous
stochastic gradient descent (SGD) based algorithms with variance reduction
converge with a linear convergent rate on convex problems. However, there is no
work to analyze asynchronous SGD with variance reduction technique on
non-convex problem. In this paper, we study two asynchronous parallel
implementations of SVRG: one is on a distributed memory system and the other is
on a shared memory system. We provide the theoretical analysis that both
algorithms can obtain a convergence rate of $O(1/T)$, and linear speed up is
achievable if the number of workers is upper bounded. V1,v2,v3 have been
withdrawn due to reference issue, please refer the newest version v4. | Asynchronous Stochastic Gradient Descent with Variance Reduction for
Non-Convex Optimization | 2,016 | http://arxiv.org/pdf/1604.03584v4 | Title Asynchronous Stochastic Gradient Descent Variance Reduction NonConvex Optimization Summary provide first theoretical analysis convergence rate asynchronous stochastic variance reduced gradient SVRG descent algorithm nonconvex optimization Recent study shown asynchronous stochastic gradient descent SGD based algorithm variance reduction converge linear convergent rate convex problem However work analyze asynchronous SGD variance reduction technique nonconvex problem paper study two asynchronous parallel implementation SVRG one distributed memory system shared memory system provide theoretical analysis algorithm obtain convergence rate O1T linear speed achievable number worker upper bounded V1v2v3 withdrawn due reference issue please refer newest version v4 Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.011296496726572514, 0.002201996510848403, -0.01778237521648407, 0.00019756263645831496, 0.0015289639122784138, -0.008500665426254272, 0.0064323665574193, -0.005723471753299236, -0.040177199989557266, 0.052204400300979614, -0.007449306081980467, 0.00011076969531131908, 0.0072547937743365765, 0.014119441621005535, -0.02469395287334919, -0.004932907409965992, 0.008514916524291039, -0.01045494619756937, -0.0017643622122704983, -0.05631594732403755, 0.04778696596622467, -0.022304555401206017, 0.008923402056097984, -0.015781855210661888, -0.009138117544353008, -0.0658765658736229, -0.0044540404342114925, 0.030186837539076805, 0.0348772257566452, -0.0024929186329245567, 0.027069834992289543, 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40,709 | 40,709 | ['Dylan Hutchison'] | 1604.04639v1 | Data scientists engage in model construction to discover machine learning
models that well explain a dataset, in terms of predictiveness,
understandability and generalization across domains. Questions such as "what if
we model common cause Z" and "what if Y's dependence on X reverses" inspire
many candidate models to consider and compare, yet current tools emphasize
constructing a final model all at once.
To more naturally reflect exploration when debating numerous models, we
propose an interactive model construction framework grounded in composable
operations. Primitive operations capture core steps refining data and model
that, when verified, form an inductive basis to prove model validity. Derived,
composite operations enable advanced model families, both generic and
specialized, abstracted away from low-level details.
We prototype our envisioned framework in ModelWizard, a domain-specific
language embedded in F# to construct Tabular models. We enumerate language
design and demonstrate its use through several applications, emphasizing how
language may facilitate creation of complex models. To future engineers
designing data science languages and tools, we offer ModelWizard's design as a
new model construction paradigm, speeding discovery of our universe's
structure. | ModelWizard: Toward Interactive Model Construction | 2,016 | http://arxiv.org/pdf/1604.04639v1 | Title ModelWizard Toward Interactive Model Construction Summary Data scientist engage model construction discover machine learning model well explain dataset term predictiveness understandability generalization across domain Questions model common cause Z Ys dependence X revers inspire many candidate model consider compare yet current tool emphasize constructing final model naturally reflect exploration debating numerous model propose interactive model construction framework grounded composable operation Primitive operation capture core step refining data model verified form inductive basis prove model validity Derived composite operation enable advanced model family generic specialized abstracted away lowlevel detail prototype envisioned framework ModelWizard domainspecific language embedded F construct Tabular model enumerate language design demonstrate use several application emphasizing language may facilitate creation complex model future engineer designing data science language tool offer ModelWizards design new model construction paradigm speeding discovery universe structure Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.0012544478522613645, 0.03426983207464218, -0.04677397757768631, -0.029425544664263725, -0.016331570222973824, -0.002292812103405595, 0.029231049120426178, -0.014060514979064465, -0.015061337500810623, -0.01203982625156641, 0.0526433028280735, 0.03149932622909546, 0.007553128059953451, 0.11196689307689667, 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40,710 | 40,710 | ['Fouad Khan'] | 1604.04893v1 | K-means is one of the most widely used clustering algorithms in various
disciplines, especially for large datasets. However the method is known to be
highly sensitive to initial seed selection of cluster centers. K-means++ has
been proposed to overcome this problem and has been shown to have better
accuracy and computational efficiency than k-means. In many clustering problems
though -such as when classifying georeferenced data for mapping applications-
standardization of clustering methodology, specifically, the ability to arrive
at the same cluster assignment for every run of the method i.e. replicability
of the methodology, may be of greater significance than any perceived measure
of accuracy, especially when the solution is known to be non-unique, as in the
case of k-means clustering. Here we propose a simple initial seed selection
algorithm for k-means clustering along one attribute that draws initial cluster
boundaries along the 'deepest valleys' or greatest gaps in dataset. Thus, it
incorporates a measure to maximize distance between consecutive cluster centers
which augments the conventional k-means optimization for minimum distance
between cluster center and cluster members. Unlike existing initialization
methods, no additional parameters or degrees of freedom are introduced to the
clustering algorithm. This improves the replicability of cluster assignments by
as much as 100% over k-means and k-means++, virtually reducing the variance
over different runs to zero, without introducing any additional parameters to
the clustering process. Further, the proposed method is more computationally
efficient than k-means++ and in some cases, more accurate. | An Initial Seed Selection Algorithm for K-means Clustering of
Georeferenced Data to Improve Replicability of Cluster Assignments for
Mapping Application | 2,016 | http://arxiv.org/pdf/1604.04893v1 | Title Initial Seed Selection Algorithm Kmeans Clustering Georeferenced Data Improve Replicability Cluster Assignments Mapping Application Summary Kmeans one widely used clustering algorithm various discipline especially large datasets However method known highly sensitive initial seed selection cluster center Kmeans proposed overcome problem shown better accuracy computational efficiency kmeans many clustering problem though classifying georeferenced data mapping application standardization clustering methodology specifically ability arrive cluster assignment every run method ie replicability methodology may greater significance perceived measure accuracy especially solution known nonunique case kmeans clustering propose simple initial seed selection algorithm kmeans clustering along one attribute draw initial cluster boundary along deepest valley greatest gap dataset Thus incorporates measure maximize distance consecutive cluster center augments conventional kmeans optimization minimum distance cluster center cluster member Unlike existing initialization method additional parameter degree freedom introduced clustering algorithm improves replicability cluster assignment much 100 kmeans kmeans virtually reducing variance different run zero without introducing additional parameter clustering process proposed method computationally efficient kmeans case accurate Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.000496017630212009, -0.013037786819040775, -0.05418940633535385, 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40,711 | 40,711 | ['Fereshteh Asgari', 'Alexis Sultan', 'Haoyi Xiong', 'Vincent Gauthier', 'Mounim El-Yacoubi'] | 1604.06577v1 | Mobile phone data have recently become an attractive source of information
about mobility behavior. Since cell phone data can be captured in a passive way
for a large user population, they can be harnessed to collect well-sampled
mobility information. In this paper, we propose CT-Mapper, an unsupervised
algorithm that enables the mapping of mobile phone traces over a multimodal
transport network. One of the main strengths of CT-Mapper is its capability to
map noisy sparse cellular multimodal trajectories over a multilayer
transportation network where the layers have different physical properties and
not only to map trajectories associated with a single layer. Such a network is
modeled by a large multilayer graph in which the nodes correspond to
metro/train stations or road intersections and edges correspond to connections
between them. The mapping problem is modeled by an unsupervised HMM where the
observations correspond to sparse user mobile trajectories and the hidden
states to the multilayer graph nodes. The HMM is unsupervised as the transition
and emission probabilities are inferred using respectively the physical
transportation properties and the information on the spatial coverage of
antenna base stations. To evaluate CT-Mapper we collected cellular traces with
their corresponding GPS trajectories for a group of volunteer users in Paris
and vicinity (France). We show that CT-Mapper is able to accurately retrieve
the real cell phone user paths despite the sparsity of the observed trace
trajectories. Furthermore our transition probability model is up to 20% more
accurate than other naive models. | CT-Mapper: Mapping Sparse Multimodal Cellular Trajectories using a
Multilayer Transportation Network | 2,016 | http://arxiv.org/pdf/1604.06577v1 | Title CTMapper Mapping Sparse Multimodal Cellular Trajectories using Multilayer Transportation Network Summary Mobile phone data recently become attractive source information mobility behavior Since cell phone data captured passive way large user population harnessed collect wellsampled mobility information paper propose CTMapper unsupervised algorithm enables mapping mobile phone trace multimodal transport network One main strength CTMapper capability map noisy sparse cellular multimodal trajectory multilayer transportation network layer different physical property map trajectory associated single layer network modeled large multilayer graph node correspond metrotrain station road intersection edge correspond connection mapping problem modeled unsupervised HMM observation correspond sparse user mobile trajectory hidden state multilayer graph node HMM unsupervised transition emission probability inferred using respectively physical transportation property information spatial coverage antenna base station evaluate CTMapper collected cellular trace corresponding GPS trajectory group volunteer user Paris vicinity France show CTMapper able accurately retrieve real cell phone user path despite sparsity observed trace trajectory Furthermore transition probability model 20 accurate naive model Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.02901574783027172, 0.03271016478538513, -0.02427433244884014, -0.034110378473997116, -0.021575668826699257, 0.002133465837687254, 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40,712 | 40,712 | ['Steffen Staab', 'Sophie Stalla-Bourdillon', 'Laura Carmichael'] | 1604.07180v1 | The research question this report addresses is: how, and to what extent,
those directly involved with the design, development and employment of a
specific black box algorithm can be certain that it is not unlawfully
discriminating (directly and/or indirectly) against particular persons with
protected characteristics (e.g. gender, race and ethnicity)? | Observing and Recommending from a Social Web with Biases | 2,016 | http://arxiv.org/pdf/1604.07180v1 | Title Observing Recommending Social Web Biases Summary research question report address extent directly involved design development employment specific black box algorithm certain unlawfully discriminating directly andor indirectly particular person protected characteristic eg gender race ethnicity Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.06716877967119217, 0.05273514986038208, -0.0263375137001276, 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40,713 | 40,713 | ['Tobias Schnabel', 'Adith Swaminathan', 'Peter Frazier', 'Thorsten Joachims'] | 1604.07209v1 | Eliciting relevance judgments for ranking evaluation is labor-intensive and
costly, motivating careful selection of which documents to judge. Unlike
traditional approaches that make this selection deterministically,
probabilistic sampling has shown intriguing promise since it enables the design
of estimators that are provably unbiased even when reusing data with missing
judgments. In this paper, we first unify and extend these sampling approaches
by viewing the evaluation problem as a Monte Carlo estimation task that applies
to a large number of common IR metrics. Drawing on the theoretical clarity that
this view offers, we tackle three practical evaluation scenarios: comparing two
systems, comparing $k$ systems against a baseline, and ranking $k$ systems. For
each scenario, we derive an estimator and a variance-optimizing sampling
distribution while retaining the strengths of sampling-based evaluation,
including unbiasedness, reusability despite missing data, and ease of use in
practice. In addition to the theoretical contribution, we empirically evaluate
our methods against previously used sampling heuristics and find that they
generally cut the number of required relevance judgments at least in half. | Unbiased Comparative Evaluation of Ranking Functions | 2,016 | http://arxiv.org/pdf/1604.07209v1 | Title Unbiased Comparative Evaluation Ranking Functions Summary Eliciting relevance judgment ranking evaluation laborintensive costly motivating careful selection document judge Unlike traditional approach make selection deterministically probabilistic sampling shown intriguing promise since enables design estimator provably unbiased even reusing data missing judgment paper first unify extend sampling approach viewing evaluation problem Monte Carlo estimation task applies large number common IR metric Drawing theoretical clarity view offer tackle three practical evaluation scenario comparing two system comparing k system baseline ranking k system scenario derive estimator varianceoptimizing sampling distribution retaining strength samplingbased evaluation including unbiasedness reusability despite missing data ease use practice addition theoretical contribution empirically evaluate method previously used sampling heuristic find generally cut number required relevance judgment least half Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.0045379395596683025, 0.027504775673151016, -0.04304656758904457, -0.038364607840776443, -0.007093917112797499, -0.0303067434579134, 0.09254908561706543, 0.005464222747832537, -0.0011169335339218378, -0.025533653795719147, 0.021620050072669983, 0.022377707064151764, 0.0512755885720253, 0.01449557300657034, -0.00261761830188334, -0.040479760617017746, 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40,714 | 40,714 | ['Edip Demirbilek', 'Jean-Charles Grégoire'] | 1604.07211v1 | We have developed reduced reference parametric models for estimating
perceived quality in audiovisual multimedia services. We have created 144
unique configurations for audiovisual content including various application and
network parameters such as bitrates and distortions in terms of bandwidth,
packet loss rate and jitter. To generate the data needed for model training and
validation we have tasked 24 subjects, in a controlled environment, to rate the
overall audiovisual quality on the absolute category rating (ACR) 5-level
quality scale. We have developed models using Random Forest and Neural Network
based machine learning methods in order to estimate Mean Opinion Scores (MOS)
values. We have used information retrieved from the packet headers and side
information provided as network parameters for model training. Random Forest
based models have performed better in terms of Root Mean Square Error (RMSE)
and Pearson correlation coefficient. The side information proved to be very
effective in developing the model. We have found that, while the model
performance might be improved by replacing the side information with more
accurate bit stream level measurements, they are performing well in estimating
perceived quality in audiovisual multimedia services. | Towards Reduced Reference Parametric Models for Estimating Audiovisual
Quality in Multimedia Services | 2,016 | http://arxiv.org/pdf/1604.07211v1 | Title Towards Reduced Reference Parametric Models Estimating Audiovisual Quality Multimedia Services Summary developed reduced reference parametric model estimating perceived quality audiovisual multimedia service created 144 unique configuration audiovisual content including various application network parameter bitrates distortion term bandwidth packet loss rate jitter generate data needed model training validation tasked 24 subject controlled environment rate overall audiovisual quality absolute category rating ACR 5level quality scale developed model using Random Forest Neural Network based machine learning method order estimate Mean Opinion Scores MOS value used information retrieved packet header side information provided network parameter model training Random Forest based model performed better term Root Mean Square Error RMSE Pearson correlation coefficient side information proved effective developing model found model performance might improved replacing side information accurate bit stream level measurement performing well estimating perceived quality audiovisual multimedia service Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.008521097712218761, -0.01335259061306715, -0.010822056792676449, -0.05868326500058174, -0.02701452188193798, -0.0311459731310606, 0.017298247665166855, 0.01732136681675911, -0.1071043610572815, -0.049301546066999435, -0.03917001932859421, -0.021798649802803993, 0.03773368149995804, 0.051910944283008575, 0.010794548317790031, -0.057460591197013855, 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40,715 | 40,715 | ['Bert J. Claessens', 'Peter Vrancx', 'Frederik Ruelens'] | 1604.08382v2 | Direct load control of a heterogeneous cluster of residential demand
flexibility sources is a high-dimensional control problem with partial
observability. This work proposes a novel approach that uses a convolutional
neural network to extract hidden state-time features to mitigate the curse of
partial observability. More specific, a convolutional neural network is used as
a function approximator to estimate the state-action value function or
Q-function in the supervised learning step of fitted Q-iteration. The approach
is evaluated in a qualitative simulation, comprising a cluster of
thermostatically controlled loads that only share their air temperature, whilst
their envelope temperature remains hidden. The simulation results show that the
presented approach is able to capture the underlying hidden features and
successfully reduce the electricity cost the cluster. | Convolutional Neural Networks For Automatic State-Time Feature
Extraction in Reinforcement Learning Applied to Residential Load Control | 2,016 | http://arxiv.org/pdf/1604.08382v2 | Title Convolutional Neural Networks Automatic StateTime Feature Extraction Reinforcement Learning Applied Residential Load Control Summary Direct load control heterogeneous cluster residential demand flexibility source highdimensional control problem partial observability work proposes novel approach us convolutional neural network extract hidden statetime feature mitigate curse partial observability specific convolutional neural network used function approximator estimate stateaction value function Qfunction supervised learning step fitted Qiteration approach evaluated qualitative simulation comprising cluster thermostatically controlled load share air temperature whilst envelope temperature remains hidden simulation result show presented approach able capture underlying hidden feature successfully reduce electricity cost cluster Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.09044639021158218, 0.019755905494093895, -0.026237109676003456, 0.05187235027551651, 0.015311677008867264, -0.013032575137913227, 0.07185986638069153, -0.0036249635741114616, 0.05736318603157997, 0.020829737186431885, -0.00842383410781622, 0.09831138700246811, -0.007240816485136747, 0.052204422652721405, 0.05242062732577324, -0.020650943741202354, 0.005850972607731819, 0.034643057733774185, 0.018103543668985367, 0.009244157932698727, -0.0033838024828583, -0.030695734545588493, 0.05740813538432121, 0.014358913525938988, -0.022674819454550743, -0.05905976518988609, 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40,716 | 40,716 | ['Z. Roshan Zamir'] | 1604.08500v1 | An epileptic seizure is a transient event of abnormal excessive neuronal
discharge in the brain. This unwanted event can be obstructed by detection of
electrical changes in the brain that happen before the seizure takes place. The
automatic detection of seizures is necessary since the visual screening of EEG
recordings is a time consuming task and requires experts to improve the
diagnosis. Four linear least squares-based preprocessing models are proposed to
extract key features of an EEG signal in order to detect seizures. The first
two models are newly developed. The original signal (EEG) is approximated by a
sinusoidal curve. Its amplitude is formed by a polynomial function and compared
with the pre developed spline function.Different statistical measures namely
classification accuracy, true positive and negative rates, false positive and
negative rates and precision are utilized to assess the performance of the
proposed models. These metrics are derived from confusion matrices obtained
from classifiers. Different classifiers are used over the original dataset and
the set of extracted features. The proposed models significantly reduce the
dimension of the classification problem and the computational time while the
classification accuracy is improved in most cases. The first and third models
are promising feature extraction methods. Logistic, LazyIB1, LazyIB5 and J48
are the best classifiers. Their true positive and negative rates are $1$ while
false positive and negative rates are zero and the corresponding precision
values are $1$. Numerical results suggest that these models are robust and
efficient for detecting epileptic seizure. | Detection of epileptic seizure in EEG signals using linear least squares
preprocessing | 2,016 | http://arxiv.org/pdf/1604.08500v1 | Title Detection epileptic seizure EEG signal using linear least square preprocessing Summary epileptic seizure transient event abnormal excessive neuronal discharge brain unwanted event obstructed detection electrical change brain happen seizure take place automatic detection seizure necessary since visual screening EEG recording time consuming task requires expert improve diagnosis Four linear least squaresbased preprocessing model proposed extract key feature EEG signal order detect seizure first two model newly developed original signal EEG approximated sinusoidal curve amplitude formed polynomial function compared pre developed spline functionDifferent statistical measure namely classification accuracy true positive negative rate false positive negative rate precision utilized ass performance proposed model metric derived confusion matrix obtained classifier Different classifier used original dataset set extracted feature proposed model significantly reduce dimension classification problem computational time classification accuracy improved case first third model promising feature extraction method Logistic LazyIB1 LazyIB5 J48 best classifier true positive negative rate 1 false positive negative rate zero corresponding precision value 1 Numerical result suggest model robust efficient detecting epileptic seizure Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.03339968994259834, -0.021695267409086227, -0.004515107721090317, 0.03286716341972351, 0.05442992225289345, 0.026823339983820915, 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40,717 | 40,717 | ['Eric Makita', 'Artem Lenskiy'] | 1604.08608v1 | Movie ratings play an important role both in determining the likelihood of a
potential viewer to watch the movie and in reflecting the current viewer
satisfaction with the movie. They are available in several sources like the
television guide, best-selling reference books, newspaper columns, and
television programs. Furthermore, movie ratings are crucial for recommendation
engines that track the behavior of all users and utilize the information to
suggest items they might like. Movie ratings in most cases, thus, provide
information that might be more important than movie feature-based data. It is
intuitively appealing that information about the viewing preferences in movie
genres is sufficient for predicting a genre of an unlabeled movie. In order to
predict movie genres, we treat ratings as a feature vector, apply the Bernoulli
event model to estimate the likelihood of a movies given genre, and evaluate
the posterior probability of the genre of a given movie using the Bayes rule.
The goal of the proposed technique is to efficiently use the movie ratings for
the task of predicting movie genres. In our approach we attempted to answer the
question: "Given the set of users who watched a movie, is it possible to
predict the genre of a movie based on its ratings?" Our simulation results with
MovieLens 100k data demonstrated the efficiency and accuracy of our proposed
technique, achieving 59% prediction rate for exact prediction and 69% when
including correlated genres. | A movie genre prediction based on Multivariate Bernoulli model and genre
correlations | 2,016 | http://arxiv.org/pdf/1604.08608v1 | Title movie genre prediction based Multivariate Bernoulli model genre correlation Summary Movie rating play important role determining likelihood potential viewer watch movie reflecting current viewer satisfaction movie available several source like television guide bestselling reference book newspaper column television program Furthermore movie rating crucial recommendation engine track behavior user utilize information suggest item might like Movie rating case thus provide information might important movie featurebased data intuitively appealing information viewing preference movie genre sufficient predicting genre unlabeled movie order predict movie genre treat rating feature vector apply Bernoulli event model estimate likelihood movie given genre evaluate posterior probability genre given movie using Bayes rule goal proposed technique efficiently use movie rating task predicting movie genre approach attempted answer question Given set user watched movie possible predict genre movie based rating simulation result MovieLens 100k data demonstrated efficiency accuracy proposed technique achieving 59 prediction rate exact prediction 69 including correlated genre Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.017057694494724274, 0.04370942711830139, -0.010268927551805973, -0.021790558472275734, 0.010173025541007519, 0.025658629834651947, 0.04486249014735222, 0.027125606313347816, -0.025045033544301987, -0.044421881437301636, -0.022357182577252388, -0.0214445311576128, -0.010712104849517345, 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40,718 | 40,718 | ['Bob L. Sturm', 'João Felipe Santos', 'Oded Ben-Tal', 'Iryna Korshunova'] | 1604.08723v1 | We apply deep learning methods, specifically long short-term memory (LSTM)
networks, to music transcription modelling and composition. We build and train
LSTM networks using approximately 23,000 music transcriptions expressed with a
high-level vocabulary (ABC notation), and use them to generate new
transcriptions. Our practical aim is to create music transcription models
useful in particular contexts of music composition. We present results from
three perspectives: 1) at the population level, comparing descriptive
statistics of the set of training transcriptions and generated transcriptions;
2) at the individual level, examining how a generated transcription reflects
the conventions of a music practice in the training transcriptions (Celtic
folk); 3) at the application level, using the system for idea generation in
music composition. We make our datasets, software and sound examples open and
available: \url{https://github.com/IraKorshunova/folk-rnn}. | Music transcription modelling and composition using deep learning | 2,016 | http://arxiv.org/pdf/1604.08723v1 | Title Music transcription modelling composition using deep learning Summary apply deep learning method specifically long shortterm memory LSTM network music transcription modelling composition build train LSTM network using approximately 23000 music transcription expressed highlevel vocabulary ABC notation use generate new transcription practical aim create music transcription model useful particular context music composition present result three perspective 1 population level comparing descriptive statistic set training transcription generated transcription 2 individual level examining generated transcription reflects convention music practice training transcription Celtic folk 3 application level using system idea generation music composition make datasets software sound example open available urlhttpsgithubcomIraKorshunovafolkrnn Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.0036442435812205076, 0.012267385609447956, 0.0007205932051874697, 0.032521460205316544, -0.025205954909324646, -0.029513472691178322, 0.03501760587096214, -0.019323060289025307, -0.05945684760808945, 0.05854586884379387, -0.07319192588329315, 0.00225464953109622, 0.042766816914081573, 0.02350153960287571, 0.017504919320344925, -0.020829282701015472, -0.004301501903682947, -0.03424189239740372, 0.01380974892526865, 0.004793661180883646, -0.028530463576316833, 0.0060548316687345505, 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40,719 | 40,719 | ['Jeroen Zegers', 'Hugo Van hamme'] | 1604.08852v1 | Non-negative Matrix Factorization (NMF) has already been applied to learn
speaker characterizations from single or non-simultaneous speech for speaker
recognition applications. It is also known for its good performance in (blind)
source separation for simultaneous speech. This paper explains how NMF can be
used to jointly solve the two problems in a multichannel speaker recognizer for
simultaneous speech. It is shown how state-of-the-art multichannel NMF for
blind source separation can be easily extended to incorporate speaker
recognition. Experiments on the CHiME corpus show that this method outperforms
the sequential approach of first applying source separation, followed by
speaker recognition that uses state-of-the-art i-vector techniques. | Joint Sound Source Separation and Speaker Recognition | 2,016 | http://arxiv.org/pdf/1604.08852v1 | Title Joint Sound Source Separation Speaker Recognition Summary Nonnegative Matrix Factorization NMF already applied learn speaker characterization single nonsimultaneous speech speaker recognition application also known good performance blind source separation simultaneous speech paper explains NMF used jointly solve two problem multichannel speaker recognizer simultaneous speech shown stateoftheart multichannel NMF blind source separation easily extended incorporate speaker recognition Experiments CHiME corpus show method outperforms sequential approach first applying source separation followed speaker recognition us stateoftheart ivector technique Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.017832355573773384, 0.027346964925527573, 0.021736089140176773, 0.04325137287378311, 0.005243368446826935, 0.005074044689536095, 0.04822973534464836, 0.022519132122397423, -0.04074070602655411, 0.00022293538495432585, -0.0782584697008133, 0.0037565501406788826, 0.04855675250291824, -0.01533531118184328, -0.010173446498811245, -0.0356551818549633, -0.017893457785248756, -0.03897685185074806, -0.022756561636924744, -0.003172982018440962, -0.006716469302773476, -0.020013095811009407, -0.07257691770792007, 0.00910115521401167, -0.04670654237270355, 0.006504732184112072, -0.018137844279408455, -0.01598905771970749, 0.009816959500312805, 0.02007935382425785, -0.02715247869491577, 0.030988577753305435, 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40,720 | 40,720 | ["Vladimir V. V'yugin"] | 0808.3746v2 | Using the game-theoretic framework for probability, Vovk and Shafer. have
shown that it is always possible, using randomization, to make sequential
probability forecasts that pass any countable set of well-behaved statistical
tests. This result generalizes work by other authors, who consider only tests
of calbration.
We complement this result with a lower bound. We show that Vovk and Shafer's
result is valid only when the forecasts are computed with unrestrictedly
increasing degree of accuracy.
When some level of discreteness is fixed, we present a game-theoretic
generalization of Oakes' example for randomized forecasting that is a test
failing any given method of deferministic forecasting; originally, this example
was presented for deterministic calibration. | A game-theoretic version of Oakes' example for randomized forecasting | 2,008 | http://arxiv.org/pdf/0808.3746v2 | Title gametheoretic version Oakes example randomized forecasting Summary Using gametheoretic framework probability Vovk Shafer shown always possible using randomization make sequential probability forecast pas countable set wellbehaved statistical test result generalizes work author consider test calbration complement result lower bound show Vovk Shafers result valid forecast computed unrestrictedly increasing degree accuracy level discreteness fixed present gametheoretic generalization Oakes example randomized forecasting test failing given method deferministic forecasting originally example presented deterministic calibration Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.009307678788900375, 0.08061213046312332, -0.02927653305232525, -0.05330027639865875, -0.044240083545446396, -0.02568724751472473, 0.044855907559394836, -0.013353520072996616, -0.00896246824413538, 0.034967150539159775, 0.06552116572856903, -0.007073864806443453, 0.033514443784952164, 0.06340785324573517, -0.02250099368393421, -0.06365387886762619, 0.005581249482929707, -0.02978624776005745, -0.013444370590150356, -0.03413739800453186, -0.023337090387940407, -0.06043057516217232, -0.010152779519557953, 0.049818530678749084, -0.0049950554966926575, -0.01600286364555359, -0.05257986858487129, -0.026657549664378166, -0.007905220612883568, -0.07996255159378052, 0.04473458230495453, -0.01668560318648815, -0.019952865317463875, 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40,721 | 40,721 | ['Maxime Berar', 'Françoise Tilotta', 'Joan Alexis Glaunès', 'Yves Rozenholc'] | 1202.2703v1 | In this paper, we present a computer-assisted method for facial
reconstruction. This method provides an estimation of the facial shape
associated with unidentified skeletal remains. Current computer-assisted
methods using a statistical framework rely on a common set of extracted points
located on the bone and soft-tissue surfaces. Most of the facial reconstruction
methods then consist of predicting the position of the soft-tissue surface
points, when the positions of the bone surface points are known. We propose to
use Latent Root Regression for prediction. The results obtained are then
compared to those given by Principal Components Analysis linear models. In
conjunction, we have evaluated the influence of the number of skull landmarks
used. Anatomical skull landmarks are completed iteratively by points located
upon geodesics which link these anatomical landmarks, thus enabling us to
artificially increase the number of skull points. Facial points are obtained
using a mesh-matching algorithm between a common reference mesh and individual
soft-tissue surface meshes. The proposed method is validated in term of
accuracy, based on a leave-one-out cross-validation test applied to a
homogeneous database. Accuracy measures are obtained by computing the distance
between the original face surface and its reconstruction. Finally, these
results are discussed referring to current computer-assisted reconstruction
facial techniques. | Craniofacial reconstruction as a prediction problem using a Latent Root
Regression model | 2,012 | http://arxiv.org/pdf/1202.2703v1 | Title Craniofacial reconstruction prediction problem using Latent Root Regression model Summary paper present computerassisted method facial reconstruction method provides estimation facial shape associated unidentified skeletal remains Current computerassisted method using statistical framework rely common set extracted point located bone softtissue surface facial reconstruction method consist predicting position softtissue surface point position bone surface point known propose use Latent Root Regression prediction result obtained compared given Principal Components Analysis linear model conjunction evaluated influence number skull landmark used Anatomical skull landmark completed iteratively point located upon geodesic link anatomical landmark thus enabling u artificially increase number skull point Facial point obtained using meshmatching algorithm common reference mesh individual softtissue surface mesh proposed method validated term accuracy based leaveoneout crossvalidation test applied homogeneous database Accuracy measure obtained computing distance original face surface reconstruction Finally result discussed referring current computerassisted reconstruction facial technique Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.050829529762268066, 0.07017198950052261, 0.011398705653846264, -0.03701702132821083, -0.007550832349807024, 0.009295189753174782, 0.02518782950937748, 0.013986041769385338, -0.051686130464076996, 0.025699570775032043, 0.05752156674861908, -0.06104416772723198, 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40,722 | 40,722 | ['Christos Boutsidis', 'Petros Drineas', 'Malik Magdon-Ismail'] | 1202.3505v2 | We study (constrained) least-squares regression as well as multiple response
least-squares regression and ask the question of whether a subset of the data,
a coreset, suffices to compute a good approximate solution to the regression.
We give deterministic, low order polynomial-time algorithms to construct such
coresets with approximation guarantees, together with lower bounds indicating
that there is not much room for improvement upon our results. | Near-optimal Coresets For Least-Squares Regression | 2,012 | http://arxiv.org/pdf/1202.3505v2 | Title Nearoptimal Coresets LeastSquares Regression Summary study constrained leastsquares regression well multiple response leastsquares regression ask question whether subset data coreset suffices compute good approximate solution regression give deterministic low order polynomialtime algorithm construct coresets approximation guarantee together lower bound indicating much room improvement upon result Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author 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40,723 | 40,723 | ['Karthekeyan Chandrasekaran', 'Richard Karp'] | 1202.3639v3 | We study the problem of learning a most biased coin among a set of coins by
tossing the coins adaptively. The goal is to minimize the number of tosses
until we identify a coin i* whose posterior probability of being most biased is
at least 1-delta for a given delta. Under a particular probabilistic model, we
give an optimal algorithm, i.e., an algorithm that minimizes the expected
number of future tosses. The problem is closely related to finding the best arm
in the multi-armed bandit problem using adaptive strategies. Our algorithm
employs an optimal adaptive strategy -- a strategy that performs the best
possible action at each step after observing the outcomes of all previous coin
tosses. Consequently, our algorithm is also optimal for any starting history of
outcomes. To our knowledge, this is the first algorithm that employs an optimal
adaptive strategy under a Bayesian setting for this problem. Our proof of
optimality employs tools from the field of Markov games. | Finding a most biased coin with fewest flips | 2,012 | http://arxiv.org/pdf/1202.3639v3 | Title Finding biased coin fewest flip Summary study problem learning biased coin among set coin tossing coin adaptively goal minimize number toss identify coin whose posterior probability biased least 1delta given delta particular probabilistic model give optimal algorithm ie algorithm minimizes expected number future toss problem closely related finding best arm multiarmed bandit problem using adaptive strategy algorithm employ optimal adaptive strategy strategy performs best possible action step observing outcome previous coin toss Consequently algorithm also optimal starting history outcome knowledge first algorithm employ optimal adaptive strategy Bayesian setting problem proof optimality employ tool field Markov game Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.026408016681671143, 0.010458852164447308, -0.01841268688440323, -0.013991071842610836, -0.039093535393476486, 0.0035663903690874577, -0.027442235499620438, 0.020405545830726624, -0.0040392219088971615, 0.005208107642829418, 0.04285596311092377, 0.03724159672856331, -0.0044845654629170895, 0.0014963611029088497, 0.02212306298315525, -0.008990572765469551, 0.003972616977989674, -0.008850788697600365, 0.07050079852342606, -0.02312929928302765, 0.019425716251134872, -0.06597866117954254, -0.059737443923950195, 0.02751813642680645, 0.023128045722842216, 0.03362167999148369, -0.0474441759288311, -0.032968830317258835, 0.014280877076089382, 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40,724 | 40,724 | ['Brendan P. W. Ames'] | 1202.3663v6 | Identifying clusters of similar objects in data plays a significant role in a
wide range of applications. As a model problem for clustering, we consider the
densest k-disjoint-clique problem, whose goal is to identify the collection of
k disjoint cliques of a given weighted complete graph maximizing the sum of the
densities of the complete subgraphs induced by these cliques. In this paper, we
establish conditions ensuring exact recovery of the densest k cliques of a
given graph from the optimal solution of a particular semidefinite program. In
particular, the semidefinite relaxation is exact for input graphs corresponding
to data consisting of k large, distinct clusters and a smaller number of
outliers. This approach also yields a semidefinite relaxation for the
biclustering problem with similar recovery guarantees. Given a set of objects
and a set of features exhibited by these objects, biclustering seeks to
simultaneously group the objects and features according to their expression
levels. This problem may be posed as partitioning the nodes of a weighted
bipartite complete graph such that the sum of the densities of the resulting
bipartite complete subgraphs is maximized. As in our analysis of the densest
k-disjoint-clique problem, we show that the correct partition of the objects
and features can be recovered from the optimal solution of a semidefinite
program in the case that the given data consists of several disjoint sets of
objects exhibiting similar features. Empirical evidence from numerical
experiments supporting these theoretical guarantees is also provided. | Guaranteed clustering and biclustering via semidefinite programming | 2,012 | http://arxiv.org/pdf/1202.3663v6 | Title Guaranteed clustering biclustering via semidefinite programming Summary Identifying cluster similar object data play significant role wide range application model problem clustering consider densest kdisjointclique problem whose goal identify collection k disjoint clique given weighted complete graph maximizing sum density complete subgraphs induced clique paper establish condition ensuring exact recovery densest k clique given graph optimal solution particular semidefinite program particular semidefinite relaxation exact input graph corresponding data consisting k large distinct cluster smaller number outlier approach also yield semidefinite relaxation biclustering problem similar recovery guarantee Given set object set feature exhibited object biclustering seek simultaneously group object feature according expression level problem may posed partitioning node weighted bipartite complete graph sum density resulting bipartite complete subgraphs maximized analysis densest kdisjointclique problem show correct partition object feature recovered optimal solution semidefinite program case given data consists several disjoint set object exhibiting similar feature Empirical evidence numerical experiment supporting theoretical guarantee also provided Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.044730715453624725, -0.03239334002137184, -0.028770793229341507, 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40,725 | 40,725 | ['Sebastien Bubeck', 'Aleksandrs Slivkins'] | 1202.4473v1 | We present a new bandit algorithm, SAO (Stochastic and Adversarial Optimal),
whose regret is, essentially, optimal both for adversarial rewards and for
stochastic rewards. Specifically, SAO combines the square-root worst-case
regret of Exp3 (Auer et al., SIAM J. on Computing 2002) and the
(poly)logarithmic regret of UCB1 (Auer et al., Machine Learning 2002) for
stochastic rewards. Adversarial rewards and stochastic rewards are the two main
settings in the literature on (non-Bayesian) multi-armed bandits. Prior work on
multi-armed bandits treats them separately, and does not attempt to jointly
optimize for both. Our result falls into a general theme of achieving good
worst-case performance while also taking advantage of "nice" problem instances,
an important issue in the design of algorithms with partially known inputs. | The best of both worlds: stochastic and adversarial bandits | 2,012 | http://arxiv.org/pdf/1202.4473v1 | Title best world stochastic adversarial bandit Summary present new bandit algorithm SAO Stochastic Adversarial Optimal whose regret essentially optimal adversarial reward stochastic reward Specifically SAO combine squareroot worstcase regret Exp3 Auer et al SIAM J Computing 2002 polylogarithmic regret UCB1 Auer et al Machine Learning 2002 stochastic reward Adversarial reward stochastic reward two main setting literature nonBayesian multiarmed bandit Prior work multiarmed bandit treat separately attempt jointly optimize result fall general theme achieving good worstcase performance also taking advantage nice problem instance important issue design algorithm partially known input Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.025592071935534477, 0.0625394657254219, 0.009916736744344234, 0.0014394497266039252, -0.008748810738325119, -0.004512563813477755, 0.034062083810567856, 0.02077368274331093, 0.004028845112770796, -0.009761612862348557, 0.0018038142006844282, -0.019352180883288383, -0.03964770585298538, 0.025637609884142876, 0.05005115270614624, 0.0038305416237562895, 0.015784237533807755, -0.03964865207672119, 0.00820754375308752, -0.022032959386706352, -0.0036004381254315376, -0.06382197141647339, -0.012481900863349438, 0.009589464403688908, -0.0009412544895894825, 0.026550471782684326, -0.036850299686193466, 0.01034301146864891, 0.03632724657654762, -0.08949963003396988, 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40,726 | 40,726 | ['Raphael Fonteneau', 'Damien Ernst', 'Bernard Boigelot', 'Quentin Louveaux'] | 1202.5298v2 | We study the minmax optimization problem introduced in [22] for computing
policies for batch mode reinforcement learning in a deterministic setting.
First, we show that this problem is NP-hard. In the two-stage case, we provide
two relaxation schemes. The first relaxation scheme works by dropping some
constraints in order to obtain a problem that is solvable in polynomial time.
The second relaxation scheme, based on a Lagrangian relaxation where all
constraints are dualized, leads to a conic quadratic programming problem. We
also theoretically prove and empirically illustrate that both relaxation
schemes provide better results than those given in [22]. | Min Max Generalization for Two-stage Deterministic Batch Mode
Reinforcement Learning: Relaxation Schemes | 2,012 | http://arxiv.org/pdf/1202.5298v2 | Title Min Max Generalization Twostage Deterministic Batch Mode Reinforcement Learning Relaxation Schemes Summary study minmax optimization problem introduced 22 computing policy batch mode reinforcement learning deterministic setting First show problem NPhard twostage case provide two relaxation scheme first relaxation scheme work dropping constraint order obtain problem solvable polynomial time second relaxation scheme based Lagrangian relaxation constraint dualized lead conic quadratic programming problem also theoretically prove empirically illustrate relaxation scheme provide better result given 22 Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao 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40,727 | 40,727 | ['Dimitrios Giannakis', 'Andrew J. Majda'] | 1202.6103v2 | We present a technique for spatiotemporal data analysis called nonlinear
Laplacian spectral analysis (NLSA), which generalizes singular spectrum
analysis (SSA) to take into account the nonlinear manifold structure of complex
data sets. The key principle underlying NLSA is that the functions used to
represent temporal patterns should exhibit a degree of smoothness on the
nonlinear data manifold M; a constraint absent from classical SSA. NLSA
enforces such a notion of smoothness by requiring that temporal patterns belong
in low-dimensional Hilbert spaces V_l spanned by the leading l Laplace-Beltrami
eigenfunctions on M. These eigenfunctions can be evaluated efficiently in high
ambient-space dimensions using sparse graph-theoretic algorithms. Moreover,
they provide orthonormal bases to expand a family of linear maps, whose
singular value decomposition leads to sets of spatiotemporal patterns at
progressively finer resolution on the data manifold. The Riemannian measure of
M and an adaptive graph kernel width enhances the capability of NLSA to detect
important nonlinear processes, including intermittency and rare events. The
minimum dimension of V_l required to capture these features while avoiding
overfitting is estimated here using spectral entropy criteria. | Nonlinear Laplacian spectral analysis: Capturing intermittent and
low-frequency spatiotemporal patterns in high-dimensional data | 2,012 | http://arxiv.org/pdf/1202.6103v2 | Title Nonlinear Laplacian spectral analysis Capturing intermittent lowfrequency spatiotemporal pattern highdimensional data Summary present technique spatiotemporal data analysis called nonlinear Laplacian spectral analysis NLSA generalizes singular spectrum analysis SSA take account nonlinear manifold structure complex data set key principle underlying NLSA function used represent temporal pattern exhibit degree smoothness nonlinear data manifold constraint absent classical SSA NLSA enforces notion smoothness requiring temporal pattern belong lowdimensional Hilbert space Vl spanned leading l LaplaceBeltrami eigenfunctions eigenfunctions evaluated efficiently high ambientspace dimension using sparse graphtheoretic algorithm Moreover provide orthonormal base expand family linear map whose singular value decomposition lead set spatiotemporal pattern progressively finer resolution data manifold Riemannian measure adaptive graph kernel width enhances capability NLSA detect important nonlinear process including intermittency rare event minimum dimension Vl required capture feature avoiding overfitting estimated using spectral entropy criterion Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.04992062598466873, -0.010343367233872414, -0.016453415155410767, 0.07195935398340225, 0.02260953187942505, -0.018110007047653198, 0.013880670070648193, 0.020548325031995773, -0.012847513891756535, -0.0065917675383389, 0.05674199014902115, -1.4577401088899933e-05, 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40,728 | 40,728 | ['Nicolas Le Roux', 'Mark Schmidt', 'Francis Bach'] | 1202.6258v4 | We propose a new stochastic gradient method for optimizing the sum of a
finite set of smooth functions, where the sum is strongly convex. While
standard stochastic gradient methods converge at sublinear rates for this
problem, the proposed method incorporates a memory of previous gradient values
in order to achieve a linear convergence rate. In a machine learning context,
numerical experiments indicate that the new algorithm can dramatically
outperform standard algorithms, both in terms of optimizing the training error
and reducing the test error quickly. | A Stochastic Gradient Method with an Exponential Convergence Rate for
Finite Training Sets | 2,012 | http://arxiv.org/pdf/1202.6258v4 | Title Stochastic Gradient Method Exponential Convergence Rate Finite Training Sets Summary propose new stochastic gradient method optimizing sum finite set smooth function sum strongly convex standard stochastic gradient method converge sublinear rate problem proposed method incorporates memory previous gradient value order achieve linear convergence rate machine learning context numerical experiment indicate new algorithm dramatically outperform standard algorithm term optimizing training error reducing test error quickly Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi 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40,729 | 40,729 | ['Nima Hatami', 'Camelia Chira', 'Giuliano Armano'] | 1206.0335v1 | Hierarchical Text Categorization (HTC) is becoming increasingly important
with the rapidly growing amount of text data available in the World Wide Web.
Among the different strategies proposed to cope with HTC, the Local Classifier
per Node (LCN) approach attains good performance by mirroring the underlying
class hierarchy while enforcing a top-down strategy in the testing step.
However, the problem of embedding hierarchical information (parent-child
relationship) to improve the performance of HTC systems still remains open. A
confidence evaluation method for a selected route in the hierarchy is proposed
to evaluate the reliability of the final candidate labels in an HTC system. In
order to take into account the information embedded in the hierarchy, weight
factors are used to take into account the importance of each level. An
acceptance/rejection strategy in the top-down decision making process is
proposed, which improves the overall categorization accuracy by rejecting a few
percentage of samples, i.e., those with low reliability score. Experimental
results on the Reuters benchmark dataset (RCV1- v2) confirm the effectiveness
of the proposed method, compared to other state-of-the art HTC methods. | A Route Confidence Evaluation Method for Reliable Hierarchical Text
Categorization | 2,012 | http://arxiv.org/pdf/1206.0335v1 | Title Route Confidence Evaluation Method Reliable Hierarchical Text Categorization Summary Hierarchical Text Categorization HTC becoming increasingly important rapidly growing amount text data available World Wide Web Among different strategy proposed cope HTC Local Classifier per Node LCN approach attains good performance mirroring underlying class hierarchy enforcing topdown strategy testing step However problem embedding hierarchical information parentchild relationship improve performance HTC system still remains open confidence evaluation method selected route hierarchy proposed evaluate reliability final candidate label HTC system order take account information embedded hierarchy weight factor used take account importance level acceptancerejection strategy topdown decision making process proposed improves overall categorization accuracy rejecting percentage sample ie low reliability score Experimental result Reuters benchmark dataset RCV1 v2 confirm effectiveness proposed method compared stateofthe art HTC method Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.0619773343205452, 0.03294936195015907, -0.011368357576429844, 0.0074493298307061195, -0.035081710666418076, 0.022727120667696, 0.01940731517970562, 0.0545775331556797, 0.057567279785871506, -0.10004162043333054, -0.018456047400832176, -0.0041920896619558334, -0.0035351496189832687, -1.3673979083250742e-05, -0.05296754464507103, -0.07695165276527405, -0.01267993077635765, 0.001315973000600934, 0.01757177896797657, 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40,730 | 40,730 | ['Mohamed Elarnaoty', 'Samir AbdelRahman', 'Aly Fahmy'] | 1206.1011v1 | Opinion mining aims at extracting useful subjective information from reliable
amounts of text. Opinion mining holder recognition is a task that has not been
considered yet in Arabic Language. This task essentially requires deep
understanding of clauses structures. Unfortunately, the lack of a robust,
publicly available, Arabic parser further complicates the research. This paper
presents a leading research for the opinion holder extraction in Arabic news
independent from any lexical parsers. We investigate constructing a
comprehensive feature set to compensate the lack of parsing structural
outcomes. The proposed feature set is tuned from English previous works coupled
with our proposed semantic field and named entities features. Our feature
analysis is based on Conditional Random Fields (CRF) and semi-supervised
pattern recognition techniques. Different research models are evaluated via
cross-validation experiments achieving 54.03 F-measure. We publicly release our
own research outcome corpus and lexicon for opinion mining community to
encourage further research. | A Machine Learning Approach For Opinion Holder Extraction In Arabic
Language | 2,012 | http://arxiv.org/pdf/1206.1011v1 | Title Machine Learning Approach Opinion Holder Extraction Arabic Language Summary Opinion mining aim extracting useful subjective information reliable amount text Opinion mining holder recognition task considered yet Arabic Language task essentially requires deep understanding clause structure Unfortunately lack robust publicly available Arabic parser complicates research paper present leading research opinion holder extraction Arabic news independent lexical parser investigate constructing comprehensive feature set compensate lack parsing structural outcome proposed feature set tuned English previous work coupled proposed semantic field named entity feature feature analysis based Conditional Random Fields CRF semisupervised pattern recognition technique Different research model evaluated via crossvalidation experiment achieving 5403 Fmeasure publicly release research outcome corpus lexicon opinion mining community encourage research Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.051405150443315506, 0.05111338570713997, 0.008856154046952724, 0.05659771338105202, -0.058939963579177856, 0.022157752886414528, 0.02627495490014553, -0.005804526619613171, -0.0028557584155350924, -0.046420641243457794, 0.001924547366797924, -0.030572574585676193, 0.005856658332049847, 0.027715357020497322, -0.05685627833008766, -0.010045782662928104, 0.012290747836232185, 0.012228288687765598, 0.06550544500350952, 0.01327830832451582, 0.05695681273937225, 0.042908601462841034, -0.012514674104750156, 0.02532738633453846, 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40,731 | 40,731 | ['Jia Zeng', 'Zhi-Qiang Liu', 'Xiao-Qin Cao'] | 1206.1147v2 | As one of the simplest probabilistic topic modeling techniques, latent
Dirichlet allocation (LDA) has found many important applications in text
mining, computer vision and computational biology. Recent training algorithms
for LDA can be interpreted within a unified message passing framework. However,
message passing requires storing previous messages with a large amount of
memory space, increasing linearly with the number of documents or the number of
topics. Therefore, the high memory usage is often a major problem for topic
modeling of massive corpora containing a large number of topics. To reduce the
space complexity, we propose a novel algorithm without storing previous
messages for training LDA: tiny belief propagation (TBP). The basic idea of TBP
relates the message passing algorithms with the non-negative matrix
factorization (NMF) algorithms, which absorb the message updating into the
message passing process, and thus avoid storing previous messages. Experimental
results on four large data sets confirm that TBP performs comparably well or
even better than current state-of-the-art training algorithms for LDA but with
a much less memory consumption. TBP can do topic modeling when massive corpora
cannot fit in the computer memory, for example, extracting thematic topics from
7 GB PUBMED corpora on a common desktop computer with 2GB memory. | Memory-Efficient Topic Modeling | 2,012 | http://arxiv.org/pdf/1206.1147v2 | Title MemoryEfficient Topic Modeling Summary one simplest probabilistic topic modeling technique latent Dirichlet allocation LDA found many important application text mining computer vision computational biology Recent training algorithm LDA interpreted within unified message passing framework However message passing requires storing previous message large amount memory space increasing linearly number document number topic Therefore high memory usage often major problem topic modeling massive corpus containing large number topic reduce space complexity propose novel algorithm without storing previous message training LDA tiny belief propagation TBP basic idea TBP relates message passing algorithm nonnegative matrix factorization NMF algorithm absorb message updating message passing process thus avoid storing previous message Experimental result four large data set confirm TBP performs comparably well even better current stateoftheart training algorithm LDA much le memory consumption TBP topic modeling massive corpus cannot fit computer memory example extracting thematic topic 7 GB PUBMED corpus common desktop computer 2GB memory Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.06376732140779495, 0.01401518750935793, 0.007114027161151171, 0.012869127094745636, -0.01643451675772667, 0.01838749274611473, -0.018147092312574387, 0.0035076874773949385, -0.04639777913689613, -0.0606553889811039, 0.010713323950767517, 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40,732 | 40,732 | ['Francesco Orabona', 'Andreas Argyriou', 'Nathan Srebro'] | 1206.2372v2 | Motivated by learning problems including max-norm regularized matrix
completion and clustering, robust PCA and sparse inverse covariance selection,
we propose a novel optimization algorithm for minimizing a convex objective
which decomposes into three parts: a smooth part, a simple non-smooth Lipschitz
part, and a simple non-smooth non-Lipschitz part. We use a time variant
smoothing strategy that allows us to obtain a guarantee that does not depend on
knowing in advance the total number of iterations nor a bound on the domain. | PRISMA: PRoximal Iterative SMoothing Algorithm | 2,012 | http://arxiv.org/pdf/1206.2372v2 | Title PRISMA PRoximal Iterative SMoothing Algorithm Summary Motivated learning problem including maxnorm regularized matrix completion clustering robust PCA sparse inverse covariance selection propose novel optimization algorithm minimizing convex objective decomposes three part smooth part simple nonsmooth Lipschitz part simple nonsmooth nonLipschitz part use time variant smoothing strategy allows u obtain guarantee depend knowing advance total number iteration bound domain Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan 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40,733 | 40,733 | ['Paolo Di Lorenzo', 'Ali H. Sayed'] | 1206.3099v2 | This article proposes diffusion LMS strategies for distributed estimation
over adaptive networks that are able to exploit sparsity in the underlying
system model. The approach relies on convex regularization, common in
compressive sensing, to enhance the detection of sparsity via a diffusive
process over the network. The resulting algorithms endow networks with learning
abilities and allow them to learn the sparse structure from the incoming data
in real-time, and also to track variations in the sparsity of the model. We
provide convergence and mean-square performance analysis of the proposed method
and show under what conditions it outperforms the unregularized diffusion
version. We also show how to adaptively select the regularization parameter.
Simulation results illustrate the advantage of the proposed filters for sparse
data recovery. | Sparse Distributed Learning Based on Diffusion Adaptation | 2,012 | http://arxiv.org/pdf/1206.3099v2 | Title Sparse Distributed Learning Based Diffusion Adaptation Summary article proposes diffusion LMS strategy distributed estimation adaptive network able exploit sparsity underlying system model approach relies convex regularization common compressive sensing enhance detection sparsity via diffusive process network resulting algorithm endow network learning ability allow learn sparse structure incoming data realtime also track variation sparsity model provide convergence meansquare performance analysis proposed method show condition outperforms unregularized diffusion version also show adaptively select regularization parameter Simulation result illustrate advantage proposed filter sparse data recovery Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.02507280558347702, 0.03188308700919151, -0.0022652570623904467, -0.0008785325335338712, 0.04222222790122032, -0.01032005064189434, 0.00030438200337812304, 0.0501880943775177, -0.023654960095882416, 0.039701562374830246, 0.030993090942502022, -0.0034217520151287317, 0.00147496466524899, -0.011308166198432446, 0.008542063646018505, 0.007840798236429691, -0.009239849634468555, 0.007884655147790909, -0.02444371208548546, -0.02861125022172928, 0.0495733805000782, -0.024581795558333397, 0.03609441965818405, -0.026611967012286186, -0.013871175236999989, 0.024927111342549324, -0.019977837800979614, 0.013255727477371693, 0.02193625271320343, -0.0489845909178257, 0.05136474594473839, 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40,734 | 40,734 | ['Pranjal Awasthi', 'Or Sheffet'] | 1206.3204v2 | Aiming to unify known results about clustering mixtures of distributions
under separation conditions, Kumar and Kannan[2010] introduced a deterministic
condition for clustering datasets. They showed that this single deterministic
condition encompasses many previously studied clustering assumptions. More
specifically, their proximity condition requires that in the target
$k$-clustering, the projection of a point $x$ onto the line joining its cluster
center $\mu$ and some other center $\mu'$, is a large additive factor closer to
$\mu$ than to $\mu'$. This additive factor can be roughly described as $k$
times the spectral norm of the matrix representing the differences between the
given (known) dataset and the means of the (unknown) target clustering.
Clearly, the proximity condition implies center separation -- the distance
between any two centers must be as large as the above mentioned bound.
In this paper we improve upon the work of Kumar and Kannan along several
axes. First, we weaken the center separation bound by a factor of $\sqrt{k}$,
and secondly we weaken the proximity condition by a factor of $k$. Using these
weaker bounds we still achieve the same guarantees when all points satisfy the
proximity condition. We also achieve better guarantees when only
$(1-\epsilon)$-fraction of the points satisfy the weaker proximity condition.
The bulk of our analysis relies only on center separation under which one can
produce a clustering which (i) has low error, (ii) has low $k$-means cost, and
(iii) has centers very close to the target centers.
Our improved separation condition allows us to match the results of the
Planted Partition Model of McSherry[2001], improve upon the results of
Ostrovsky et al[2006], and improve separation results for mixture of Gaussian
models in a particular setting. | Improved Spectral-Norm Bounds for Clustering | 2,012 | http://arxiv.org/pdf/1206.3204v2 | Title Improved SpectralNorm Bounds Clustering Summary Aiming unify known result clustering mixture distribution separation condition Kumar Kannan2010 introduced deterministic condition clustering datasets showed single deterministic condition encompasses many previously studied clustering assumption specifically proximity condition requires target kclustering projection point x onto line joining cluster center mu center mu large additive factor closer mu mu additive factor roughly described k time spectral norm matrix representing difference given known dataset mean unknown target clustering Clearly proximity condition implies center separation distance two center must large mentioned bound paper improve upon work Kumar Kannan along several ax First weaken center separation bound factor sqrtk secondly weaken proximity condition factor k Using weaker bound still achieve guarantee point satisfy proximity condition also achieve better guarantee 1epsilonfraction point satisfy weaker proximity condition bulk analysis relies center separation one produce clustering low error ii low kmeans cost iii center close target center improved separation condition allows u match result Planted Partition Model McSherry2001 improve upon result Ostrovsky et al2006 improve separation result mixture Gaussian model particular setting Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.018541967496275902, -0.03669000789523125, -0.025297053158283234, 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-0.0028076577000319958, -0.04353892058134079, 0.017073536291718483] |
40,735 | 40,735 | ['Saurabh Sarkar', 'Prateek Malhotra', 'Virender Guman'] | 1206.3509v1 | The idea of this project is to study the protein structure and sequence
relationship using the hidden markov model and artificial neural network. In
this context we have assumed two hidden markov models. In first model we have
taken protein secondary structures as hidden and protein sequences as observed.
In second model we have taken protein sequences as hidden and protein
structures as observed. The efficiencies for both the hidden markov models have
been calculated. The results show that the efficiencies of first model is
greater that the second one .These efficiencies are cross validated using
artificial neural network. This signifies the importance of protein secondary
structures as the main hidden controlling factors due to which we observe a
particular amino acid sequence. This also signifies that protein secondary
structure is more conserved in comparison to amino acid sequence. | A Novel Approach for Protein Structure Prediction | 2,012 | http://arxiv.org/pdf/1206.3509v1 | Title Novel Approach Protein Structure Prediction Summary idea project study protein structure sequence relationship using hidden markov model artificial neural network context assumed two hidden markov model first model taken protein secondary structure hidden protein sequence observed second model taken protein sequence hidden protein structure observed efficiency hidden markov model calculated result show efficiency first model greater second one efficiency cross validated using artificial neural network signifies importance protein secondary structure main hidden controlling factor due observe particular amino acid sequence also signifies protein secondary structure conserved comparison amino acid sequence Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.01838711090385914, 0.026679471135139465, -0.004050067160278559, -0.004913358483463526, -0.02182084135711193, -0.022608622908592224, -0.03912850096821785, 0.027435390278697014, 0.011451302096247673, -0.006600531283766031, 0.016494881361722946, -0.012174826115369797, 0.05589022487401962, 0.030094608664512634, 0.010616594925522804, 0.0258890762925148, -0.0008856093045324087, 0.0002583775785751641, -0.0018526293570175767, -0.0009571507689543068, 0.0011001980165019631, -0.032679468393325806, 0.023740030825138092, 0.01491154171526432, 0.01972971297800541, 0.04300479590892792, -0.030789820477366447, -0.0191500186920166, 0.013792742043733597, 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40,736 | 40,736 | ['Tayfun Gürel', 'Carsten Mehring'] | 1206.3666v1 | The performance of neural decoders can degrade over time due to
nonstationarities in the relationship between neuronal activity and behavior.
In this case, brain-machine interfaces (BMI) require adaptation of their
decoders to maintain high performance across time. One way to achieve this is
by use of periodical calibration phases, during which the BMI system (or an
external human demonstrator) instructs the user to perform certain movements or
behaviors. This approach has two disadvantages: (i) calibration phases
interrupt the autonomous operation of the BMI and (ii) between two calibration
phases the BMI performance might not be stable but continuously decrease. A
better alternative would be that the BMI decoder is able to continuously adapt
in an unsupervised manner during autonomous BMI operation, i.e. without knowing
the movement intentions of the user.
In the present article, we present an efficient method for such unsupervised
training of BMI systems for continuous movement control. The proposed method
utilizes a cost function derived from neuronal recordings, which guides a
learning algorithm to evaluate the decoding parameters. We verify the
performance of our adaptive method by simulating a BMI user with an optimal
feedback control model and its interaction with our adaptive BMI decoder. The
simulation results show that the cost function and the algorithm yield fast and
precise trajectories towards targets at random orientations on a 2-dimensional
computer screen. For initially unknown and non-stationary tuning parameters,
our unsupervised method is still able to generate precise trajectories and to
keep its performance stable in the long term. The algorithm can optionally work
also with neuronal error signals instead or in conjunction with the proposed
unsupervised adaptation. | Unsupervised adaptation of brain machine interface decoders | 2,012 | http://arxiv.org/pdf/1206.3666v1 | Title Unsupervised adaptation brain machine interface decoder Summary performance neural decoder degrade time due nonstationarities relationship neuronal activity behavior case brainmachine interface BMI require adaptation decoder maintain high performance across time One way achieve use periodical calibration phase BMI system external human demonstrator instructs user perform certain movement behavior approach two disadvantage calibration phase interrupt autonomous operation BMI ii two calibration phase BMI performance might stable continuously decrease better alternative would BMI decoder able continuously adapt unsupervised manner autonomous BMI operation ie without knowing movement intention user present article present efficient method unsupervised training BMI system continuous movement control proposed method utilizes cost function derived neuronal recording guide learning algorithm evaluate decoding parameter verify performance adaptive method simulating BMI user optimal feedback control model interaction adaptive BMI decoder simulation result show cost function algorithm yield fast precise trajectory towards target random orientation 2dimensional computer screen initially unknown nonstationary tuning parameter unsupervised method still able generate precise trajectory keep performance stable long term algorithm optionally work also neuronal error signal instead conjunction proposed unsupervised adaptation Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name 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40,737 | 40,737 | ['Truyen T. Tran', 'Duc Son Pham'] | 1206.4110v1 | We propose a new data mining approach in ranking documents based on the
concept of cone-based generalized inequalities between vectors. A partial
ordering between two vectors is made with respect to a proper cone and thus
learning the preferences is formulated as learning proper cones. A pairwise
learning-to-rank algorithm (ConeRank) is proposed to learn a non-negative
subspace, formulated as a polyhedral cone, over document-pair differences. The
algorithm is regularized by controlling the `volume' of the cone. The
experimental studies on the latest and largest ranking dataset LETOR 4.0 shows
that ConeRank is competitive against other recent ranking approaches. | ConeRANK: Ranking as Learning Generalized Inequalities | 2,012 | http://arxiv.org/pdf/1206.4110v1 | Title ConeRANK Ranking Learning Generalized Inequalities Summary propose new data mining approach ranking document based concept conebased generalized inequality vector partial ordering two vector made respect proper cone thus learning preference formulated learning proper cone pairwise learningtorank algorithm ConeRank proposed learn nonnegative subspace formulated polyhedral cone documentpair difference algorithm regularized controlling volume cone experimental study latest largest ranking dataset LETOR 40 show ConeRank competitive recent ranking approach Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.031192472204566002, 0.015477515757083893, -0.03223806247115135, 0.03891593590378761, 0.0018668549600988626, -0.0012175246374681592, -0.02656860463321209, 0.026169080287218094, -0.03710814565420151, -0.03888426721096039, 0.002084981417283416, 0.009120646864175797, -0.005115026608109474, -0.021435827016830444, -0.02307862415909767, -0.043399278074502945, 0.005631803069263697, 0.012401687912642956, -0.014253588393330574, -0.01048516109585762, 0.009560734033584595, -0.04920263960957527, -0.00827940646559, 0.006741790566593409, 0.026662424206733704, 0.01318826712667942, -0.003002570942044258, -0.03358302265405655, 0.05561093986034393, -0.06632360816001892, 0.037253476679325104, 0.02639443799853325, 0.0012031567748636007, -0.012075653299689293, 2.2068475118430797e-06, -0.04820471629500389, 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40,738 | 40,738 | ['M. Fauvel', 'A. Villa', 'J. Chanussot', 'J. A. Benediktsson'] | 1206.4481v2 | The classification of high dimensional data with kernel methods is considered
in this article. Exploit- ing the emptiness property of high dimensional
spaces, a kernel based on the Mahalanobis distance is proposed. The computation
of the Mahalanobis distance requires the inversion of a covariance matrix. In
high dimensional spaces, the estimated covariance matrix is ill-conditioned and
its inversion is unstable or impossible. Using a parsimonious statistical
model, namely the High Dimensional Discriminant Analysis model, the specific
signal and noise subspaces are estimated for each considered class making the
inverse of the class specific covariance matrix explicit and stable, leading to
the definition of a parsimonious Mahalanobis kernel. A SVM based framework is
used for selecting the hyperparameters of the parsimonious Mahalanobis kernel
by optimizing the so-called radius-margin bound. Experimental results on three
high dimensional data sets show that the proposed kernel is suitable for
classifying high dimensional data, providing better classification accuracies
than the conventional Gaussian kernel. | Parsimonious Mahalanobis Kernel for the Classification of High
Dimensional Data | 2,012 | http://arxiv.org/pdf/1206.4481v2 | Title Parsimonious Mahalanobis Kernel Classification High Dimensional Data Summary classification high dimensional data kernel method considered article Exploit ing emptiness property high dimensional space kernel based Mahalanobis distance proposed computation Mahalanobis distance requires inversion covariance matrix high dimensional space estimated covariance matrix illconditioned inversion unstable impossible Using parsimonious statistical model namely High Dimensional Discriminant Analysis model specific signal noise subspace estimated considered class making inverse class specific covariance matrix explicit stable leading definition parsimonious Mahalanobis kernel SVM based framework used selecting hyperparameters parsimonious Mahalanobis kernel optimizing socalled radiusmargin bound Experimental result three high dimensional data set show proposed kernel suitable classifying high dimensional data providing better classification accuracy conventional Gaussian kernel Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.038560900837183, -0.032875560224056244, -0.034856200218200684, 0.0356142558157444, 0.00118716387078166, 0.012616604566574097, 0.04123619571328163, 0.05087288096547127, 0.019785329699516296, 0.009697537869215012, -0.00125721818767488, 0.030180973932147026, 8.260575123131275e-05, 0.03025691583752632, 0.01534024253487587, -0.022517161443829536, -0.012799629010260105, -0.0059153544716537, -0.05004960671067238, -0.01908423751592636, -0.005626756697893143, -0.025428781285881996, 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40,739 | 40,739 | ['Elad Hazan', 'Satyen Kale'] | 1206.4657v1 | The computational bottleneck in applying online learning to massive data sets
is usually the projection step. We present efficient online learning algorithms
that eschew projections in favor of much more efficient linear optimization
steps using the Frank-Wolfe technique. We obtain a range of regret bounds for
online convex optimization, with better bounds for specific cases such as
stochastic online smooth convex optimization.
Besides the computational advantage, other desirable features of our
algorithms are that they are parameter-free in the stochastic case and produce
sparse decisions. We apply our algorithms to computationally intensive
applications of collaborative filtering, and show the theoretical improvements
to be clearly visible on standard datasets. | Projection-free Online Learning | 2,012 | http://arxiv.org/pdf/1206.4657v1 | Title Projectionfree Online Learning Summary computational bottleneck applying online learning massive data set usually projection step present efficient online learning algorithm eschew projection favor much efficient linear optimization step using FrankWolfe technique obtain range regret bound online convex optimization better bound specific case stochastic online smooth convex optimization Besides computational advantage desirable feature algorithm parameterfree stochastic case produce sparse decision apply algorithm computationally intensive application collaborative filtering show theoretical improvement clearly visible standard datasets Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.040699802339076996, 0.07614393532276154, -0.00592581182718277, -0.008099240250885487, -0.015378925018012524, 0.0018427150789648294, 0.03405725955963135, 0.005123643204569817, -0.028440317139029503, -0.00027435991796664894, 0.02447696402668953, 0.007107550278306007, 0.009734504856169224, 0.06414944678544998, -0.01436102855950594, -0.055566634982824326, -0.026184963062405586, -0.010164034552872181, -0.012625165283679962, -0.04694050922989845, 0.010675478726625443, -0.000512423284817487, -0.012681077234447002, 0.020010463893413544, -0.07783077657222748, -0.021584229543805122, -0.02340444177389145, -0.05145438760519028, -0.024867098778486252, -0.016168249770998955, 0.005783026106655598, -0.03828180581331253, 0.004157924093306065, 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40,740 | 40,740 | ['Sanjeev Arora', 'Rong Ge', 'Ankur Moitra', 'Sushant Sachdeva'] | 1206.5349v2 | We present a new algorithm for Independent Component Analysis (ICA) which has
provable performance guarantees. In particular, suppose we are given samples of
the form $y = Ax + \eta$ where $A$ is an unknown $n \times n$ matrix and $x$ is
a random variable whose components are independent and have a fourth moment
strictly less than that of a standard Gaussian random variable and $\eta$ is an
$n$-dimensional Gaussian random variable with unknown covariance $\Sigma$: We
give an algorithm that provable recovers $A$ and $\Sigma$ up to an additive
$\epsilon$ and whose running time and sample complexity are polynomial in $n$
and $1 / \epsilon$. To accomplish this, we introduce a novel "quasi-whitening"
step that may be useful in other contexts in which the covariance of Gaussian
noise is not known in advance. We also give a general framework for finding all
local optima of a function (given an oracle for approximately finding just one)
and this is a crucial step in our algorithm, one that has been overlooked in
previous attempts, and allows us to control the accumulation of error when we
find the columns of $A$ one by one via local search. | Provable ICA with Unknown Gaussian Noise, and Implications for Gaussian
Mixtures and Autoencoders | 2,012 | http://arxiv.org/pdf/1206.5349v2 | Title Provable ICA Unknown Gaussian Noise Implications Gaussian Mixtures Autoencoders Summary present new algorithm Independent Component Analysis ICA provable performance guarantee particular suppose given sample form Ax eta unknown n time n matrix x random variable whose component independent fourth moment strictly le standard Gaussian random variable eta ndimensional Gaussian random variable unknown covariance Sigma give algorithm provable recovers Sigma additive epsilon whose running time sample complexity polynomial n 1 epsilon accomplish introduce novel quasiwhitening step may useful context covariance Gaussian noise known advance also give general framework finding local optimum function given oracle approximately finding one crucial step algorithm one overlooked previous attempt allows u control accumulation error find column one one via local search Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.015556919388473034, 0.0677202045917511, 0.0013297570403665304, 0.09801431745290756, 0.021335741505026817, 0.015658937394618988, 0.05631428584456444, 0.03125451132655144, 0.003295156406238675, 0.034231603145599365, 0.006459851283580065, 0.011376420967280865, 0.02920609340071678, 0.048263825476169586, 0.008115386590361595, 0.018658336251974106, -0.048341166228055954, -0.046756044030189514, 0.012622041627764702, -0.0591125451028347, -0.009799626655876637, -0.06477134674787521, -0.009713651612401009, -0.02880294993519783, -0.0826946422457695, -0.0030129735823720694, 0.019251909106969833, 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40,741 | 40,741 | ['Pierre-François Marteau', 'Nicolas Bonnel', 'Gilbas Ménier'] | 1206.6196v1 | This paper proposes a framework dedicated to the construction of what we call
discrete elastic inner product allowing one to embed sets of non-uniformly
sampled multivariate time series or sequences of varying lengths into inner
product space structures. This framework is based on a recursive definition
that covers the case of multiple embedded time elastic dimensions. We prove
that such inner products exist in our general framework and show how a simple
instance of this inner product class operates on some prospective applications,
while generalizing the Euclidean inner product. Classification experimentations
on time series and symbolic sequences datasets demonstrate the benefits that we
can expect by embedding time series or sequences into elastic inner spaces
rather than into classical Euclidean spaces. These experiments show good
accuracy when compared to the euclidean distance or even dynamic programming
algorithms while maintaining a linear algorithmic complexity at exploitation
stage, although a quadratic indexing phase beforehand is required. | Discrete Elastic Inner Vector Spaces with Application in Time Series and
Sequence Mining | 2,012 | http://arxiv.org/pdf/1206.6196v1 | Title Discrete Elastic Inner Vector Spaces Application Time Series Sequence Mining Summary paper proposes framework dedicated construction call discrete elastic inner product allowing one embed set nonuniformly sampled multivariate time series sequence varying length inner product space structure framework based recursive definition cover case multiple embedded time elastic dimension prove inner product exist general framework show simple instance inner product class operates prospective application generalizing Euclidean inner product Classification experimentation time series symbolic sequence datasets demonstrate benefit expect embedding time series sequence elastic inner space rather classical Euclidean space experiment show good accuracy compared euclidean distance even dynamic programming algorithm maintaining linear algorithmic complexity exploitation stage although quadratic indexing phase beforehand required Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.06303804367780685, 0.05267355591058731, -0.061134956777095795, 0.0010639370884746313, -0.006786917801946402, 0.04177102446556091, 0.02916567213833332, 0.03502098098397255, -0.005986991338431835, 0.006148139014840126, 0.08487411588430405, 0.0032025608234107494, -0.020162852481007576, 0.011798912659287453, 0.013486037030816078, -0.05592140927910805, 0.04153629019856453, 0.035789813846349716, 0.029473207890987396, 0.023642312735319138, -0.0022501135244965553, -0.040090564638376236, -0.023550434038043022, 0.0383821576833725, 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40,742 | 40,742 | ['Guy Lebanon'] | 1206.6858v1 | The popular bag of words assumption represents a document as a histogram of
word occurrences. While computationally efficient, such a representation is
unable to maintain any sequential information. We present a continuous and
differentiable sequential document representation that goes beyond the bag of
words assumption, and yet is efficient and effective. This representation
employs smooth curves in the multinomial simplex to account for sequential
information. We discuss the representation and its geometric properties and
demonstrate its applicability for the task of text classification. | Sequential Document Representations and Simplicial Curves | 2,012 | http://arxiv.org/pdf/1206.6858v1 | Title Sequential Document Representations Simplicial Curves Summary popular bag word assumption represents document histogram word occurrence computationally efficient representation unable maintain sequential information present continuous differentiable sequential document representation go beyond bag word assumption yet efficient effective representation employ smooth curve multinomial simplex account sequential information discus representation geometric property demonstrate applicability task text classification Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.015401527285575867, 0.016077682375907898, -0.01316965650767088, 0.03996576368808746, -0.057235293090343475, 0.01861460693180561, 0.028477970510721207, 0.05515775457024574, -0.003781169420108199, -0.037801966071128845, 0.03465668857097626, 0.001212308299727738, -0.002086876193061471, 0.03812525048851967, -0.03196542337536812, -0.07841010391712189, 0.013197977095842361, 0.007573798764497042, -0.004557563457638025, 0.027618762105703354, 0.03186008334159851, -0.014079056680202484, -0.017853431403636932, 0.006540767848491669, -0.027623377740383148, -0.04148808866739273, -0.01999514549970627, -0.03751426935195923, 0.0027580163441598415, -0.04026314616203308, 0.02404368482530117, 0.04716608300805092, 0.0238829143345356, -0.01998068206012249, 1.920478553074645e-06, -0.029404927045106888, -0.04889539256691933, -0.01088710967451334, 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40,743 | 40,743 | ['Chunxiao Jiang', 'Yan Chen', 'K. J. Ray Liu'] | 1212.1245v2 | Distributed adaptive filtering has been considered as an effective approach
for data processing and estimation over distributed networks. Most existing
distributed adaptive filtering algorithms focus on designing different
information diffusion rules, regardless of the nature evolutionary
characteristic of a distributed network. In this paper, we study the adaptive
network from the game theoretic perspective and formulate the distributed
adaptive filtering problem as a graphical evolutionary game. With the proposed
formulation, the nodes in the network are regarded as players and the local
combiner of estimation information from different neighbors is regarded as
different strategies selection. We show that this graphical evolutionary game
framework is very general and can unify the existing adaptive network
algorithms. Based on this framework, as examples, we further propose two
error-aware adaptive filtering algorithms. Moreover, we use graphical
evolutionary game theory to analyze the information diffusion process over the
adaptive networks and evolutionarily stable strategy of the system. Finally,
simulation results are shown to verify the effectiveness of our analysis and
proposed methods. | Distributed Adaptive Networks: A Graphical Evolutionary Game-Theoretic
View | 2,012 | http://arxiv.org/pdf/1212.1245v2 | Title Distributed Adaptive Networks Graphical Evolutionary GameTheoretic View Summary Distributed adaptive filtering considered effective approach data processing estimation distributed network existing distributed adaptive filtering algorithm focus designing different information diffusion rule regardless nature evolutionary characteristic distributed network paper study adaptive network game theoretic perspective formulate distributed adaptive filtering problem graphical evolutionary game proposed formulation node network regarded player local combiner estimation information different neighbor regarded different strategy selection show graphical evolutionary game framework general unify existing adaptive network algorithm Based framework example propose two erroraware adaptive filtering algorithm Moreover use graphical evolutionary game theory analyze information diffusion process adaptive network evolutionarily stable strategy system Finally simulation result shown verify effectiveness analysis proposed method Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.026787932962179184, -0.020158153027296066, -0.01999885030090809, -0.036404140293598175, -0.012417173013091087, -0.036921177059412, 0.03650304675102234, -0.012925592251121998, 0.011567372828722, 0.012604085728526115, 0.004656721372157335, 0.03015720658004284, 0.022845223546028137, 0.010832652449607849, -0.0013786056078970432, -0.0005936652887612581, -0.02961023896932602, 0.022963527590036392, 0.012372076511383057, -0.03493805602192879, 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40,744 | 40,744 | ['Yuval Rabani', 'Leonard Schulman', 'Chaitanya Swamy'] | 1212.1527v3 | We give an algorithm for learning a mixture of {\em unstructured}
distributions. This problem arises in various unsupervised learning scenarios,
for example in learning {\em topic models} from a corpus of documents spanning
several topics. We show how to learn the constituents of a mixture of $k$
arbitrary distributions over a large discrete domain $[n]=\{1,2,\dots,n\}$ and
the mixture weights, using $O(n\polylog n)$ samples. (In the topic-model
learning setting, the mixture constituents correspond to the topic
distributions.) This task is information-theoretically impossible for $k>1$
under the usual sampling process from a mixture distribution. However, there
are situations (such as the above-mentioned topic model case) in which each
sample point consists of several observations from the same mixture
constituent. This number of observations, which we call the {\em "sampling
aperture"}, is a crucial parameter of the problem. We obtain the {\em first}
bounds for this mixture-learning problem {\em without imposing any assumptions
on the mixture constituents.} We show that efficient learning is possible
exactly at the information-theoretically least-possible aperture of $2k-1$.
Thus, we achieve near-optimal dependence on $n$ and optimal aperture. While the
sample-size required by our algorithm depends exponentially on $k$, we prove
that such a dependence is {\em unavoidable} when one considers general
mixtures. A sequence of tools contribute to the algorithm, such as
concentration results for random matrices, dimension reduction, moment
estimations, and sensitivity analysis. | Learning Mixtures of Arbitrary Distributions over Large Discrete Domains | 2,012 | http://arxiv.org/pdf/1212.1527v3 | Title Learning Mixtures Arbitrary Distributions Large Discrete Domains Summary give algorithm learning mixture em unstructured distribution problem arises various unsupervised learning scenario example learning em topic model corpus document spanning several topic show learn constituent mixture k arbitrary distribution large discrete domain n12dotsn mixture weight using Onpolylog n sample topicmodel learning setting mixture constituent correspond topic distribution task informationtheoretically impossible k1 usual sampling process mixture distribution However situation abovementioned topic model case sample point consists several observation mixture constituent number observation call em sampling aperture crucial parameter problem obtain em first bound mixturelearning problem em without imposing assumption mixture constituent show efficient learning possible exactly informationtheoretically leastpossible aperture 2k1 Thus achieve nearoptimal dependence n optimal aperture samplesize required algorithm depends exponentially k prove dependence em unavoidable one considers general mixture sequence tool contribute algorithm concentration result random matrix dimension reduction moment estimation sensitivity analysis Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.007280575577169657, -0.0005570410285145044, -0.014140112325549126, 0.017895173281431198, -0.013171542435884476, 0.014481115154922009, 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40,745 | 40,745 | ['Maher Salem', 'Ulrich Buehler'] | 1212.2414v1 | In intrusion detection systems, classifiers still suffer from several
drawbacks such as data dimensionality and dominance, different network feature
types, and data impact on the classification. In this paper two significant
enhancements are presented to solve these drawbacks. The first enhancement is
an improved feature selection using sequential backward search and information
gain. This, in turn, extracts valuable features that enhance positively the
detection rate and reduce the false positive rate. The second enhancement is
transferring nominal network features to numeric ones by exploiting the
discrete random variable and the probability mass function to solve the problem
of different feature types, the problem of data dominance, and data impact on
the classification. The latter is combined to known normalization methods to
achieve a significant hybrid normalization approach. Finally, an intensive and
comparative study approves the efficiency of these enhancements and shows
better performance comparing to other proposed methods. | Mining Techniques in Network Security to Enhance Intrusion Detection
Systems | 2,012 | http://arxiv.org/pdf/1212.2414v1 | Title Mining Techniques Network Security Enhance Intrusion Detection Systems Summary intrusion detection system classifier still suffer several drawback data dimensionality dominance different network feature type data impact classification paper two significant enhancement presented solve drawback first enhancement improved feature selection using sequential backward search information gain turn extract valuable feature enhance positively detection rate reduce false positive rate second enhancement transferring nominal network feature numeric one exploiting discrete random variable probability mass function solve problem different feature type problem data dominance data impact classification latter combined known normalization method achieve significant hybrid normalization approach Finally intensive comparative study approves efficiency enhancement show better performance comparing proposed method Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.010694855824112892, 0.01289962325245142, -0.02200631983578205, 0.04416825622320175, -0.04733153060078621, -0.04085034877061844, 0.04067566618323326, 0.0008969876216724515, 0.03506622090935707, -0.030060384422540665, 0.025059208273887634, -0.0008643663604743779, -0.010369261726737022, 0.04680173099040985, -0.029801206663250923, -0.008914311416447163, 0.041155800223350525, -0.012732497416436672, 0.028280654922127724, 0.034366365522146225, 0.01490715891122818, -0.021229222416877747, 0.006406879052519798, 0.0549311637878418, 0.01285325363278389, 0.01410259772092104, 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40,746 | 40,746 | ['Gregory Lawrence', 'Noah Cowan', 'Stuart Russell'] | 1212.2475v1 | The task of estimating the gradient of a function in the presence of noise is
central to several forms of reinforcement learning, including policy search
methods. We present two techniques for reducing gradient estimation errors in
the presence of observable input noise applied to the control signal. The first
method extends the idea of a reinforcement baseline by fitting a local linear
model to the function whose gradient is being estimated; we show how to find
the linear model that minimizes the variance of the gradient estimate, and how
to estimate the model from data. The second method improves this further by
discounting components of the gradient vector that have high variance. These
methods are applied to the problem of motor control learning, where actuator
noise has a significant influence on behavior. In particular, we apply the
techniques to learn locally optimal controllers for a dart-throwing task using
a simulated three-link arm; we demonstrate that proposed methods significantly
improve the reward function gradient estimate and, consequently, the learning
curve, over existing methods. | Efficient Gradient Estimation for Motor Control Learning | 2,012 | http://arxiv.org/pdf/1212.2475v1 | Title Efficient Gradient Estimation Motor Control Learning Summary task estimating gradient function presence noise central several form reinforcement learning including policy search method present two technique reducing gradient estimation error presence observable input noise applied control signal first method extends idea reinforcement baseline fitting local linear model function whose gradient estimated show find linear model minimizes variance gradient estimate estimate model data second method improves discounting component gradient vector high variance method applied problem motor control learning actuator noise significant influence behavior particular apply technique learn locally optimal controller dartthrowing task using simulated threelink arm demonstrate proposed method significantly improve reward function gradient estimate consequently learning curve existing method Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.0012196259340271354, -0.010438837110996246, -0.0014591384679079056, -0.00038575372309423983, 0.026463791728019714, -0.018245398998260498, -0.029572971165180206, 0.021115966141223907, -0.01647784747183323, 0.003600427648052573, -0.057980090379714966, 0.023918692022562027, 0.014457684941589832, -2.7942545784753747e-05, 0.038508858531713486, -0.03670504689216614, 0.03310123085975647, 0.005028921645134687, 0.06728401780128479, -0.008209259249269962, 0.0490957610309124, -0.01689283922314644, 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40,747 | 40,747 | ['Anirban Majumder', 'Nisheeth Shrivastava'] | 1212.3390v1 | Online service platforms (OSPs), such as search engines, news-websites,
ad-providers, etc., serve highly pe rsonalized content to the user, based on
the profile extracted from his history with the OSP. Although personalization
(generally) leads to a better user experience, it also raises privacy concerns
for the user---he does not know what is present in his profile and more
importantly, what is being used to per sonalize content for him. In this paper,
we capture OSP's personalization for an user in a new data structure called the
person alization vector ($\eta$), which is a weighted vector over a set of
topics, and present techniques to compute it for users of an OSP. Our approach
treats OSPs as black-boxes, and extracts $\eta$ by mining only their output,
specifical ly, the personalized (for an user) and vanilla (without any user
information) contents served, and the differences in these content. We
formulate a new model called Latent Topic Personalization (LTP) that captures
the personalization vector into a learning framework and present efficient
inference algorithms for it. We do extensive experiments for search result
personalization using both data from real Google users and synthetic datasets.
Our results show high accuracy (R-pre = 84%) of LTP in finding personalized
topics. For Google data, our qualitative results show how LTP can also
identifies evidences---queries for results on a topic with high $\eta$ value
were re-ranked. Finally, we show how our approach can be used to build a new
Privacy evaluation framework focused at end-user privacy on commercial OSPs. | Know Your Personalization: Learning Topic level Personalization in
Online Services | 2,012 | http://arxiv.org/pdf/1212.3390v1 | Title Know Personalization Learning Topic level Personalization Online Services Summary Online service platform OSPs search engine newswebsites adproviders etc serve highly pe rsonalized content user based profile extracted history OSP Although personalization generally lead better user experience also raise privacy concern userhe know present profile importantly used per sonalize content paper capture OSPs personalization user new data structure called person alization vector eta weighted vector set topic present technique compute user OSP approach treat OSPs blackboxes extract eta mining output specifical ly personalized user vanilla without user information content served difference content formulate new model called Latent Topic Personalization LTP capture personalization vector learning framework present efficient inference algorithm extensive experiment search result personalization using data real Google user synthetic datasets result show high accuracy Rpre 84 LTP finding personalized topic Google data qualitative result show LTP also identifies evidencesqueries result topic high eta value reranked Finally show approach used build new Privacy evaluation framework focused enduser privacy commercial OSPs Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.06459541618824005, 0.029003649950027466, -0.04012618958950043, 0.013461176306009293, -0.012808429077267647, -0.030821193009614944, 0.03935445100069046, -0.01081411074846983, 0.005793794058263302, -0.042772077023983, 0.00182656932156533, 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40,748 | 40,748 | ['Gabriele Modena'] | 1212.3669v2 | Vulnerability discovery and exploits detection are two wide areas of study in
software engineering. This preliminary work tries to combine existing methods
with machine learning techniques to define a metric classification of
vulnerable computer programs. First a feature set has been defined and later
two models have been tested against real world vulnerabilities. A relation
between the classifier choice and the features has also been outlined. | A metric for software vulnerabilities classification | 2,012 | http://arxiv.org/pdf/1212.3669v2 | Title metric software vulnerability classification Summary Vulnerability discovery exploit detection two wide area study software engineering preliminary work try combine existing method machine learning technique define metric classification vulnerable computer program First feature set defined later two model tested real world vulnerability relation classifier choice feature also outlined Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author 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40,749 | 40,749 | ['Elaine Tsiang'] | 1212.5091v1 | We formulate the problem of perception in the framework of information
theory, and prove that categorical perception is equivalent to the existence of
an observable that has the maximum possible information on the target of
perception. We call such an observable maximally informative. Regardless
whether categorical perception is real, maximally informative observables can
form the basis of a theory of perception. We conclude with the implications of
such a theory for the problem of speech perception. | Maximally Informative Observables and Categorical Perception | 2,012 | http://arxiv.org/pdf/1212.5091v1 | Title Maximally Informative Observables Categorical Perception Summary formulate problem perception framework information theory prove categorical perception equivalent existence observable maximum possible information target perception call observable maximally informative Regardless whether categorical perception real maximally informative observables form basis theory perception conclude implication theory problem speech perception Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen 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40,750 | 40,750 | ['K. Dhanalakshmi', 'H. Hannah Inbarani'] | 1212.5359v1 | Clustering is one of the widely used data mining techniques for medical
diagnosis. Clustering can be considered as the most important unsupervised
learning technique. Most of the clustering methods group data based on distance
and few methods cluster data based on similarity. The clustering algorithms
classify gene expression data into clusters and the functionally related genes
are grouped together in an efficient manner. The groupings are constructed such
that the degree of relationship is strong among members of the same cluster and
weak among members of different clusters. In this work, we focus on a
similarity relationship among genes with similar expression patterns so that a
consequential and simple analytical decision can be made from the proposed
Fuzzy Soft Rough K-Means algorithm. The algorithm is developed based on Fuzzy
Soft sets and Rough sets. Comparative analysis of the proposed work is made
with bench mark algorithms like K-Means and Rough K-Means and efficiency of the
proposed algorithm is illustrated in this work by using various cluster
validity measures such as DB index and Xie-Beni index. | Fuzzy soft rough K-Means clustering approach for gene expression data | 2,012 | http://arxiv.org/pdf/1212.5359v1 | Title Fuzzy soft rough KMeans clustering approach gene expression data Summary Clustering one widely used data mining technique medical diagnosis Clustering considered important unsupervised learning technique clustering method group data based distance method cluster data based similarity clustering algorithm classify gene expression data cluster functionally related gene grouped together efficient manner grouping constructed degree relationship strong among member cluster weak among member different cluster work focus similarity relationship among gene similar expression pattern consequential simple analytical decision made proposed Fuzzy Soft Rough KMeans algorithm algorithm developed based Fuzzy Soft set Rough set Comparative analysis proposed work made bench mark algorithm like KMeans Rough KMeans efficiency proposed algorithm illustrated work using various cluster validity measure DB index XieBeni index Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.015309467911720276, -0.015365765430033207, -0.06365137547254562, -0.01609174720942974, 0.01800129935145378, 0.019789697602391243, 0.041180361062288284, 0.07750619202852249, -0.004956546239554882, 0.004681665450334549, 0.03730899840593338, 0.018867071717977524, 0.009532786905765533, 0.038073763251304626, -0.00012819308903999627, 0.021862326189875603, 0.01107085682451725, 0.02769632451236248, 0.03292078524827957, 0.009143867529928684, 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40,751 | 40,751 | ['G. Jothi', 'H. Hannah Inbarani'] | 1212.5391v1 | Lung cancer is the deadliest type of cancer for both men and women. Feature
selection plays a vital role in cancer classification. This paper investigates
the feature selection process in Computed Tomographic (CT) lung cancer images
using soft set theory. We propose a new soft set based unsupervised feature
selection algorithm. Nineteen features are extracted from the segmented lung
images using gray level co-occurence matrix (GLCM) and gray level different
matrix (GLDM). In this paper, an efficient Unsupervised Soft Set based Quick
Reduct (SSUSQR) algorithm is presented. This method is used to select features
from the data set and compared with existing rough set based unsupervised
feature selection methods. Then K-Means and Self Organizing Map (SOM)
clustering algorithms are used to cluster the data. The performance of the
feature selection algorithms is evaluated based on performance of clustering
techniques. The results show that the proposed method effectively removes
redundant features. | Soft Set Based Feature Selection Approach for Lung Cancer Images | 2,012 | http://arxiv.org/pdf/1212.5391v1 | Title Soft Set Based Feature Selection Approach Lung Cancer Images Summary Lung cancer deadliest type cancer men woman Feature selection play vital role cancer classification paper investigates feature selection process Computed Tomographic CT lung cancer image using soft set theory propose new soft set based unsupervised feature selection algorithm Nineteen feature extracted segmented lung image using gray level cooccurence matrix GLCM gray level different matrix GLDM paper efficient Unsupervised Soft Set based Quick Reduct SSUSQR algorithm presented method used select feature data set compared existing rough set based unsupervised feature selection method KMeans Self Organizing Map SOM clustering algorithm used cluster data performance feature selection algorithm evaluated based performance clustering technique result show proposed method effectively remove redundant feature Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.041185908019542694, -0.005432871636003256, -0.051971834152936935, -0.024186445400118828, -0.009373980574309826, 0.07036839425563812, 0.04865609109401703, 0.015852896496653557, -0.010408642701804638, 0.024665703997015953, 0.021352307870984077, -0.006262725684791803, 0.0339340977370739, 0.039282508194446564, 0.00023065440473146737, -0.04244076833128929, -0.008678396232426167, 0.012347436510026455, 0.02352827787399292, -0.006766048260033131, 0.0075539774261415005, 0.004810696002095938, 0.006881571840494871, 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40,752 | 40,752 | ['Olivier Sprangers', 'Gabriel A. D. Lopes', 'Robert Babuska'] | 1212.5524v2 | Passivity-based control (PBC) for port-Hamiltonian systems provides an
intuitive way of achieving stabilization by rendering a system passive with
respect to a desired storage function. However, in most instances the control
law is obtained without any performance considerations and it has to be
calculated by solving a complex partial differential equation (PDE). In order
to address these issues we introduce a reinforcement learning approach into the
energy-balancing passivity-based control (EB-PBC) method, which is a form of
PBC in which the closed-loop energy is equal to the difference between the
stored and supplied energies. We propose a technique to parameterize EB-PBC
that preserves the systems's PDE matching conditions, does not require the
specification of a global desired Hamiltonian, includes performance criteria,
and is robust to extra non-linearities such as control input saturation. The
parameters of the control law are found using actor-critic reinforcement
learning, enabling learning near-optimal control policies satisfying a desired
closed-loop energy landscape. The advantages are that near-optimal controllers
can be generated using standard energy shaping techniques and that the
solutions learned can be interpreted in terms of energy shaping and damping
injection, which makes it possible to numerically assess stability using
passivity theory. From the reinforcement learning perspective, our proposal
allows for the class of port-Hamiltonian systems to be incorporated in the
actor-critic framework, speeding up the learning thanks to the resulting
parameterization of the policy. The method has been successfully applied to the
pendulum swing-up problem in simulations and real-life experiments. | Reinforcement learning for port-Hamiltonian systems | 2,012 | http://arxiv.org/pdf/1212.5524v2 | Title Reinforcement learning portHamiltonian system Summary Passivitybased control PBC portHamiltonian system provides intuitive way achieving stabilization rendering system passive respect desired storage function However instance control law obtained without performance consideration calculated solving complex partial differential equation PDE order address issue introduce reinforcement learning approach energybalancing passivitybased control EBPBC method form PBC closedloop energy equal difference stored supplied energy propose technique parameterize EBPBC preserve system PDE matching condition require specification global desired Hamiltonian includes performance criterion robust extra nonlinearities control input saturation parameter control law found using actorcritic reinforcement learning enabling learning nearoptimal control policy satisfying desired closedloop energy landscape advantage nearoptimal controller generated using standard energy shaping technique solution learned interpreted term energy shaping damping injection make possible numerically ass stability using passivity theory reinforcement learning perspective proposal allows class portHamiltonian system incorporated actorcritic framework speeding learning thanks resulting parameterization policy method successfully applied pendulum swingup problem simulation reallife experiment Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype 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40,753 | 40,753 | ['Farid Beninel', 'Waad Bouaguel', 'Ghazi Belmufti'] | 1212.6167v1 | The credit scoring risk management is a fast growing field due to consumer's
credit requests. Credit requests, of new and existing customers, are often
evaluated by classical discrimination rules based on customers information.
However, these kinds of strategies have serious limits and don't take into
account the characteristics difference between current customers and the future
ones. The aim of this paper is to measure credit worthiness for non customers
borrowers and to model potential risk given a heterogeneous population formed
by borrowers customers of the bank and others who are not. We hold on previous
works done in generalized gaussian discrimination and transpose them into the
logistic model to bring out efficient discrimination rules for non customers'
subpopulation.
Therefore we obtain several simple models of connection between parameters of
both logistic models associated respectively to the two subpopulations. The
German credit data set is selected to experiment and to compare these models.
Experimental results show that the use of links between the two subpopulations
improve the classification accuracy for the new loan applicants. | Transfer Learning Using Logistic Regression in Credit Scoring | 2,012 | http://arxiv.org/pdf/1212.6167v1 | Title Transfer Learning Using Logistic Regression Credit Scoring Summary credit scoring risk management fast growing field due consumer credit request Credit request new existing customer often evaluated classical discrimination rule based customer information However kind strategy serious limit dont take account characteristic difference current customer future one aim paper measure credit worthiness non customer borrower model potential risk given heterogeneous population formed borrower customer bank others hold previous work done generalized gaussian discrimination transpose logistic model bring efficient discrimination rule non customer subpopulation Therefore obtain several simple model connection parameter logistic model associated respectively two subpopulation German credit data set selected experiment compare model Experimental result show use link two subpopulation improve classification accuracy new loan applicant Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.005685362499207258, 0.06497889757156372, -0.02461809478700161, -0.02646719478070736, 0.026342609897255898, 0.05330488458275795, 0.060300860553979874, 0.04384007304906845, 0.014535575173795223, -0.02516884356737137, -0.006618023384362459, -0.053027406334877014, 0.026856781914830208, 0.03391644358634949, -0.004445609170943499, -0.013789691962301731, 0.03440986946225166, 0.0297201219946146, -0.03243526816368103, 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40,754 | 40,754 | ['Geoffrey J. Gordon'] | 1212.6958v1 | We present a new interior-point potential-reduction algorithm for solving
monotone linear complementarity problems (LCPs) that have a particular special
structure: their matrix $M\in{\mathbb R}^{n\times n}$ can be decomposed as
$M=\Phi U + \Pi_0$, where the rank of $\Phi$ is $k<n$, and $\Pi_0$ denotes
Euclidean projection onto the nullspace of $\Phi^\top$. We call such LCPs
projective. Our algorithm solves a monotone projective LCP to relative accuracy
$\epsilon$ in $O(\sqrt n \ln(1/\epsilon))$ iterations, with each iteration
requiring $O(nk^2)$ flops. This complexity compares favorably with
interior-point algorithms for general monotone LCPs: these algorithms also
require $O(\sqrt n \ln(1/\epsilon))$ iterations, but each iteration needs to
solve an $n\times n$ system of linear equations, a much higher cost than our
algorithm when $k\ll n$. Our algorithm works even though the solution to a
projective LCP is not restricted to lie in any low-rank subspace. | Fast Solutions to Projective Monotone Linear Complementarity Problems | 2,012 | http://arxiv.org/pdf/1212.6958v1 | Title Fast Solutions Projective Monotone Linear Complementarity Problems Summary present new interiorpoint potentialreduction algorithm solving monotone linear complementarity problem LCPs particular special structure matrix Minmathbb Rntimes n decomposed MPhi U Pi0 rank Phi kn Pi0 denotes Euclidean projection onto nullspace Phitop call LCPs projective algorithm solves monotone projective LCP relative accuracy epsilon Osqrt n ln1epsilon iteration iteration requiring Onk2 flop complexity compare favorably interiorpoint algorithm general monotone LCPs algorithm also require Osqrt n ln1epsilon iteration iteration need solve ntimes n system linear equation much higher cost algorithm kll n algorithm work even though solution projective LCP restricted lie lowrank subspace Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.04336011782288551, 0.028920181095600128, -0.0079445019364357, 0.018053486943244934, -0.08390683680772781, -0.0350223034620285, -0.023664021864533424, 0.03730517625808716, -0.016077537089586258, -0.011217656545341015, 0.057939790189266205, 0.056402090936899185, 0.009005350060760975, -0.03925344720482826, 0.0038944671396166086, -0.024005480110645294, -0.00643318984657526, -0.005190388765186071, -0.0986068844795227, -0.05183323100209236, -0.009180279448628426, -0.017975790426135063, -0.02102835476398468, -0.04870443418622017, -0.016257282346487045, -0.03657199814915657, 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40,755 | 40,755 | ['Ankur Moitra', 'Michael Saks'] | 1302.1515v2 | We give a polynomial time algorithm for the lossy population recovery
problem. In this problem, the goal is to approximately learn an unknown
distribution on binary strings of length $n$ from lossy samples: for some
parameter $\mu$ each coordinate of the sample is preserved with probability
$\mu$ and otherwise is replaced by a `?'. The running time and number of
samples needed for our algorithm is polynomial in $n$ and $1/\varepsilon$ for
each fixed $\mu>0$. This improves on algorithm of Wigderson and Yehudayoff that
runs in quasi-polynomial time for any $\mu > 0$ and the polynomial time
algorithm of Dvir et al which was shown to work for $\mu \gtrapprox 0.30$ by
Batman et al. In fact, our algorithm also works in the more general framework
of Batman et al. in which there is no a priori bound on the size of the support
of the distribution. The algorithm we analyze is implicit in previous work; our
main contribution is to analyze the algorithm by showing (via linear
programming duality and connections to complex analysis) that a certain matrix
associated with the problem has a robust local inverse even though its
condition number is exponentially small. A corollary of our result is the first
polynomial time algorithm for learning DNFs in the restriction access model of
Dvir et al. | A Polynomial Time Algorithm for Lossy Population Recovery | 2,013 | http://arxiv.org/pdf/1302.1515v2 | Title Polynomial Time Algorithm Lossy Population Recovery Summary give polynomial time algorithm lossy population recovery problem problem goal approximately learn unknown distribution binary string length n lossy sample parameter mu coordinate sample preserved probability mu otherwise replaced running time number sample needed algorithm polynomial n 1varepsilon fixed mu0 improves algorithm Wigderson Yehudayoff run quasipolynomial time mu 0 polynomial time algorithm Dvir et al shown work mu gtrapprox 030 Batman et al fact algorithm also work general framework Batman et al priori bound size support distribution algorithm analyze implicit previous work main contribution analyze algorithm showing via linear programming duality connection complex analysis certain matrix associated problem robust local inverse even though condition number exponentially small corollary result first polynomial time algorithm learning DNFs restriction access model Dvir et al Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.008139933459460735, 0.08294190466403961, -0.006523034535348415, 0.004406788852065802, -0.012195287272334099, -0.020927580073475838, -0.028986576944589615, 0.05296475440263748, -0.046328116208314896, 0.037264127284288406, 0.0806245282292366, -0.0044210441410541534, 0.006243383977562189, 0.030934443697333336, 0.038927365094423294, -0.0384160578250885, -0.039673686027526855, 0.02064627781510353, -0.022032881155610085, -0.010675828903913498, 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40,756 | 40,756 | ['Kai-Yang Chiang', 'Cho-Jui Hsieh', 'Nagarajan Natarajan', 'Ambuj Tewari', 'Inderjit S. Dhillon'] | 1302.5145v2 | The study of social networks is a burgeoning research area. However, most
existing work deals with networks that simply encode whether relationships
exist or not. In contrast, relationships in signed networks can be positive
("like", "trust") or negative ("dislike", "distrust"). The theory of social
balance shows that signed networks tend to conform to some local patterns that,
in turn, induce certain global characteristics. In this paper, we exploit both
local as well as global aspects of social balance theory for two fundamental
problems in the analysis of signed networks: sign prediction and clustering.
Motivated by local patterns of social balance, we first propose two families of
sign prediction methods: measures of social imbalance (MOIs), and supervised
learning using high order cycles (HOCs). These methods predict signs of edges
based on triangles and \ell-cycles for relatively small values of \ell.
Interestingly, by examining measures of social imbalance, we show that the
classic Katz measure, which is used widely in unsigned link prediction,
actually has a balance theoretic interpretation when applied to signed
networks. Furthermore, motivated by the global structure of balanced networks,
we propose an effective low rank modeling approach for both sign prediction and
clustering. For the low rank modeling approach, we provide theoretical
performance guarantees via convex relaxations, scale it up to large problem
sizes using a matrix factorization based algorithm, and provide extensive
experimental validation including comparisons with local approaches. Our
experimental results indicate that, by adopting a more global viewpoint of
balance structure, we get significant performance and computational gains in
prediction and clustering tasks on signed networks. Our work therefore
highlights the usefulness of the global aspect of balance theory for the
analysis of signed networks. | Prediction and Clustering in Signed Networks: A Local to Global
Perspective | 2,013 | http://arxiv.org/pdf/1302.5145v2 | Title Prediction Clustering Signed Networks Local Global Perspective Summary study social network burgeoning research area However existing work deal network simply encode whether relationship exist contrast relationship signed network positive like trust negative dislike distrust theory social balance show signed network tend conform local pattern turn induce certain global characteristic paper exploit local well global aspect social balance theory two fundamental problem analysis signed network sign prediction clustering Motivated local pattern social balance first propose two family sign prediction method measure social imbalance MOIs supervised learning using high order cycle HOCs method predict sign edge based triangle ellcycles relatively small value ell Interestingly examining measure social imbalance show classic Katz measure used widely unsigned link prediction actually balance theoretic interpretation applied signed network Furthermore motivated global structure balanced network propose effective low rank modeling approach sign prediction clustering low rank modeling approach provide theoretical performance guarantee via convex relaxation scale large problem size using matrix factorization based algorithm provide extensive experimental validation including comparison local approach experimental result indicate adopting global viewpoint balance structure get significant performance computational gain prediction clustering task signed network work therefore highlight usefulness global aspect balance theory analysis signed network Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England 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40,757 | 40,757 | ['Mohammed El Anbari', 'Abdallah Mkhadri'] | 1302.6390v1 | We consider the problem of variables selection and estimation in linear
regression model in situations where the number of parameters diverges with the
sample size. We propose the adaptive Generalized Ridge-Lasso (\mbox{AdaGril})
which is an extension of the the adaptive Elastic Net. AdaGril incorporates
information redundancy among correlated variables for model selection and
estimation. It combines the strengths of the quadratic regularization and the
adaptively weighted Lasso shrinkage. In this paper, we highlight the grouped
selection property for AdaCnet method (one type of AdaGril) in the equal
correlation case. Under weak conditions, we establish the oracle property of
AdaGril which ensures the optimal large performance when the dimension is high.
Consequently, it achieves both goals of handling the problem of collinearity in
high dimension and enjoys the oracle property. Moreover, we show that AdaGril
estimator achieves a Sparsity Inequality, i. e., a bound in terms of the number
of non-zero components of the 'true' regression coefficient. This bound is
obtained under a similar weak Restricted Eigenvalue (RE) condition used for
Lasso. Simulations studies show that some particular cases of AdaGril
outperform its competitors. | The adaptive Gril estimator with a diverging number of parameters | 2,013 | http://arxiv.org/pdf/1302.6390v1 | Title adaptive Gril estimator diverging number parameter Summary consider problem variable selection estimation linear regression model situation number parameter diverges sample size propose adaptive Generalized RidgeLasso mboxAdaGril extension adaptive Elastic Net AdaGril incorporates information redundancy among correlated variable model selection estimation combine strength quadratic regularization adaptively weighted Lasso shrinkage paper highlight grouped selection property AdaCnet method one type AdaGril equal correlation case weak condition establish oracle property AdaGril ensures optimal large performance dimension high Consequently achieves goal handling problem collinearity high dimension enjoys oracle property Moreover show AdaGril estimator achieves Sparsity Inequality e bound term number nonzero component true regression coefficient bound obtained similar weak Restricted Eigenvalue condition used Lasso Simulations study show particular case AdaGril outperform competitor Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.02800752967596054, 0.05801106244325638, -0.028368467465043068, -0.012728462927043438, 0.031293317675590515, 0.021327709779143333, 0.07747331261634827, 0.012708493508398533, 0.027218785136938095, 0.04184013977646828, 0.024207377806305885, 0.05397256463766098, 0.013556001707911491, 4.0731101762503386e-05, 0.0698462575674057, 0.0019813845865428448, -0.007946673780679703, 0.008263498544692993, 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40,758 | 40,758 | ['Jónathan Heras', 'Ekaterina Komendantskaya'] | 1302.6421v3 | ML4PG is a machine-learning extension that provides statistical proof hints
during the process of Coq/SSReflect proof development. In this paper, we use
ML4PG to find proof patterns in the CoqEAL library -- a library that was
devised to verify the correctness of Computer Algebra algorithms. In
particular, we use ML4PG to help us in the formalisation of an efficient
algorithm to compute the inverse of triangular matrices. | ML4PG in Computer Algebra verification | 2,013 | http://arxiv.org/pdf/1302.6421v3 | Title ML4PG Computer Algebra verification Summary ML4PG machinelearning extension provides statistical proof hint process CoqSSReflect proof development paper use ML4PG find proof pattern CoqEAL library library devised verify correctness Computer Algebra algorithm particular use ML4PG help u formalisation efficient algorithm compute inverse triangular matrix Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.01857442408800125, 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40,759 | 40,759 | ['Emad M. Grais', 'Hakan Erdogan'] | 1302.7283v1 | We propose a new method to enforce priors on the solution of the nonnegative
matrix factorization (NMF). The proposed algorithm can be used for denoising or
single-channel source separation (SCSS) applications. The NMF solution is
guided to follow the Minimum Mean Square Error (MMSE) estimates under Gaussian
mixture prior models (GMM) for the source signal. In SCSS applications, the
spectra of the observed mixed signal are decomposed as a weighted linear
combination of trained basis vectors for each source using NMF. In this work,
the NMF decomposition weight matrices are treated as a distorted image by a
distortion operator, which is learned directly from the observed signals. The
MMSE estimate of the weights matrix under GMM prior and log-normal distribution
for the distortion is then found to improve the NMF decomposition results. The
MMSE estimate is embedded within the optimization objective to form a novel
regularized NMF cost function. The corresponding update rules for the new
objectives are derived in this paper. Experimental results show that, the
proposed regularized NMF algorithm improves the source separation performance
compared with using NMF without prior or with other prior models. | Source Separation using Regularized NMF with MMSE Estimates under GMM
Priors with Online Learning for The Uncertainties | 2,013 | http://arxiv.org/pdf/1302.7283v1 | Title Source Separation using Regularized NMF MMSE Estimates GMM Priors Online Learning Uncertainties Summary propose new method enforce prior solution nonnegative matrix factorization NMF proposed algorithm used denoising singlechannel source separation SCSS application NMF solution guided follow Minimum Mean Square Error MMSE estimate Gaussian mixture prior model GMM source signal SCSS application spectrum observed mixed signal decomposed weighted linear combination trained basis vector source using NMF work NMF decomposition weight matrix treated distorted image distortion operator learned directly observed signal MMSE estimate weight matrix GMM prior lognormal distribution distortion found improve NMF decomposition result MMSE estimate embedded within optimization objective form novel regularized NMF cost function corresponding update rule new objective derived paper Experimental result show proposed regularized NMF algorithm improves source separation performance compared using NMF without prior prior model Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.0182354636490345, 0.04698843136429787, 0.010780141688883305, 0.00808739010244608, 0.05170631781220436, -0.038233086466789246, 0.03760913386940956, 0.04141865670681, -0.06561922281980515, -0.0036247018724679947, -0.026902563869953156, 0.03622301667928696, 0.008255423977971077, -0.0027204526122659445, 0.010053287260234356, -0.004870472941547632, -0.00818229652941227, -0.03735015541315079, -0.0175179336220026, 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40,760 | 40,760 | ['Shafigh Parsazad', 'Ehsan Saboori', 'Amin Allahyar'] | 1305.2388v1 | In the most intrusion detection systems (IDS), a system tries to learn
characteristics of different type of attacks by analyzing packets that sent or
received in network. These packets have a lot of features. But not all of them
is required to be analyzed to detect that specific type of attack. Detection
speed and computational cost is another vital matter here, because in these
types of problems, datasets are very huge regularly. In this paper we tried to
propose a very simple and fast feature selection method to eliminate features
with no helpful information on them. Result faster learning in process of
redundant feature omission. We compared our proposed method with three most
successful similarity based feature selection algorithm including Correlation
Coefficient, Least Square Regression Error and Maximal Information Compression
Index. After that we used recommended features by each of these algorithms in
two popular classifiers including: Bayes and KNN classifier to measure the
quality of the recommendations. Experimental result shows that although the
proposed method can't outperform evaluated algorithms with high differences in
accuracy, but in computational cost it has huge superiority over them. | Fast Feature Reduction in intrusion detection datasets | 2,013 | http://arxiv.org/pdf/1305.2388v1 | Title Fast Feature Reduction intrusion detection datasets Summary intrusion detection system IDS system try learn characteristic different type attack analyzing packet sent received network packet lot feature required analyzed detect specific type attack Detection speed computational cost another vital matter type problem datasets huge regularly paper tried propose simple fast feature selection method eliminate feature helpful information Result faster learning process redundant feature omission compared proposed method three successful similarity based feature selection algorithm including Correlation Coefficient Least Square Regression Error Maximal Information Compression Index used recommended feature algorithm two popular classifier including Bayes KNN classifier measure quality recommendation Experimental result show although proposed method cant outperform evaluated algorithm high difference accuracy computational cost huge superiority Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.04138384386897087, -0.009275500662624836, -0.0299786776304245, 0.04195195809006691, -0.06028420850634575, -0.014267221093177795, 0.07615303248167038, 0.013218099251389503, 0.030329516157507896, -0.027706192806363106, 0.03114135190844536, 0.02593553625047207, 0.013898496516048908, 0.09561406821012497, -0.0242844857275486, 0.010679120197892189, 0.01980794407427311, 0.01339852437376976, 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40,761 | 40,761 | ['Ashwinkumar Badanidiyuru', 'Robert Kleinberg', 'Aleksandrs Slivkins'] | 1305.2545v8 | Multi-armed bandit problems are the predominant theoretical model of
exploration-exploitation tradeoffs in learning, and they have countless
applications ranging from medical trials, to communication networks, to Web
search and advertising. In many of these application domains the learner may be
constrained by one or more supply (or budget) limits, in addition to the
customary limitation on the time horizon. The literature lacks a general model
encompassing these sorts of problems. We introduce such a model, called
"bandits with knapsacks", that combines aspects of stochastic integer
programming with online learning. A distinctive feature of our problem, in
comparison to the existing regret-minimization literature, is that the optimal
policy for a given latent distribution may significantly outperform the policy
that plays the optimal fixed arm. Consequently, achieving sublinear regret in
the bandits-with-knapsacks problem is significantly more challenging than in
conventional bandit problems.
We present two algorithms whose reward is close to the information-theoretic
optimum: one is based on a novel "balanced exploration" paradigm, while the
other is a primal-dual algorithm that uses multiplicative updates. Further, we
prove that the regret achieved by both algorithms is optimal up to
polylogarithmic factors. We illustrate the generality of the problem by
presenting applications in a number of different domains including electronic
commerce, routing, and scheduling. As one example of a concrete application, we
consider the problem of dynamic posted pricing with limited supply and obtain
the first algorithm whose regret, with respect to the optimal dynamic policy,
is sublinear in the supply. | Bandits with Knapsacks | 2,013 | http://arxiv.org/pdf/1305.2545v8 | Title Bandits Knapsacks Summary Multiarmed bandit problem predominant theoretical model explorationexploitation tradeoff learning countless application ranging medical trial communication network Web search advertising many application domain learner may constrained one supply budget limit addition customary limitation time horizon literature lack general model encompassing sort problem introduce model called bandit knapsack combine aspect stochastic integer programming online learning distinctive feature problem comparison existing regretminimization literature optimal policy given latent distribution may significantly outperform policy play optimal fixed arm Consequently achieving sublinear regret banditswithknapsacks problem significantly challenging conventional bandit problem present two algorithm whose reward close informationtheoretic optimum one based novel balanced exploration paradigm primaldual algorithm us multiplicative update prove regret achieved algorithm optimal polylogarithmic factor illustrate generality problem presenting application number different domain including electronic commerce routing scheduling one example concrete application consider problem dynamic posted pricing limited supply obtain first algorithm whose regret respect optimal dynamic policy sublinear supply Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.037161316722631454, 0.05644448474049568, -0.015572826378047466, -0.0565476194024086, -0.010808642953634262, 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40,762 | 40,762 | ['Fabian Pedregosa', 'Michael Eickenberg', 'Bertrand Thirion', 'Alexandre Gramfort'] | 1305.2788v1 | Extracting activation patterns from functional Magnetic Resonance Images
(fMRI) datasets remains challenging in rapid-event designs due to the inherent
delay of blood oxygen level-dependent (BOLD) signal. The general linear model
(GLM) allows to estimate the activation from a design matrix and a fixed
hemodynamic response function (HRF). However, the HRF is known to vary
substantially between subjects and brain regions. In this paper, we propose a
model for jointly estimating the hemodynamic response function (HRF) and the
activation patterns via a low-rank representation of task effects.This model is
based on the linearity assumption behind the GLM and can be computed using
standard gradient-based solvers. We use the activation patterns computed by our
model as input data for encoding and decoding studies and report performance
improvement in both settings. | HRF estimation improves sensitivity of fMRI encoding and decoding models | 2,013 | http://arxiv.org/pdf/1305.2788v1 | Title HRF estimation improves sensitivity fMRI encoding decoding model Summary Extracting activation pattern functional Magnetic Resonance Images fMRI datasets remains challenging rapidevent design due inherent delay blood oxygen leveldependent BOLD signal general linear model GLM allows estimate activation design matrix fixed hemodynamic response function HRF However HRF known vary substantially subject brain region paper propose model jointly estimating hemodynamic response function HRF activation pattern via lowrank representation task effectsThis model based linearity assumption behind GLM computed using standard gradientbased solver use activation pattern computed model input data encoding decoding study report performance improvement setting Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.019836314022541046, 0.03579607978463173, -0.018655018880963326, 0.00684982817620039, 0.011454648338258266, 0.029941964894533157, 0.027283674106001854, 0.08468989282846451, 0.03735656663775444, 0.019732432439923286, -0.05029069632291794, -0.0556279756128788, 0.06845329701900482, 0.03579476475715637, 0.033700186759233475, -0.011192704550921917, -0.004720608703792095, 0.005869872868061066, 0.04249608889222145, -0.040939897298812866, -0.0036599072627723217, -0.013984983786940575, -0.03011341020464897, 0.008230010978877544, 0.011155158281326294, -0.04288974776864052, 0.03496906906366348, -0.01843414269387722, 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40,763 | 40,763 | ['Kuang-Chih Lee', 'Ali Jalali', 'Ali Dasdan'] | 1305.3011v1 | Today, billions of display ad impressions are purchased on a daily basis
through a public auction hosted by real time bidding (RTB) exchanges. A
decision has to be made for advertisers to submit a bid for each selected RTB
ad request in milliseconds. Restricted by the budget, the goal is to buy a set
of ad impressions to reach as many targeted users as possible. A desired action
(conversion), advertiser specific, includes purchasing a product, filling out a
form, signing up for emails, etc. In addition, advertisers typically prefer to
spend their budget smoothly over the time in order to reach a wider range of
audience accessible throughout a day and have a sustainable impact. However,
since the conversions occur rarely and the occurrence feedback is normally
delayed, it is very challenging to achieve both budget and performance goals at
the same time. In this paper, we present an online approach to the smooth
budget delivery while optimizing for the conversion performance. Our algorithm
tries to select high quality impressions and adjust the bid price based on the
prior performance distribution in an adaptive manner by distributing the budget
optimally across time. Our experimental results from real advertising campaigns
demonstrate the effectiveness of our proposed approach. | Real Time Bid Optimization with Smooth Budget Delivery in Online
Advertising | 2,013 | http://arxiv.org/pdf/1305.3011v1 | Title Real Time Bid Optimization Smooth Budget Delivery Online Advertising Summary Today billion display ad impression purchased daily basis public auction hosted real time bidding RTB exchange decision made advertiser submit bid selected RTB ad request millisecond Restricted budget goal buy set ad impression reach many targeted user possible desired action conversion advertiser specific includes purchasing product filling form signing email etc addition advertiser typically prefer spend budget smoothly time order reach wider range audience accessible throughout day sustainable impact However since conversion occur rarely occurrence feedback normally delayed challenging achieve budget performance goal time paper present online approach smooth budget delivery optimizing conversion performance algorithm try select high quality impression adjust bid price based prior performance distribution adaptive manner distributing budget optimally across time experimental result real advertising campaign demonstrate effectiveness proposed approach Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.07785237580537796, 0.028655359521508217, -0.03451156243681908, -0.05659424886107445, -0.011497131548821926, -0.03385933116078377, 0.020923320204019547, -0.0011330408742651343, -0.05761895701289177, -0.06000596657395363, -0.04220873862504959, -0.06761900335550308, 0.02234136499464512, 0.13113738596439362, -0.016431182622909546, -0.08869611471891403, -0.025098737329244614, -0.014992181211709976, -0.02310447208583355, 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40,764 | 40,764 | ['Ali Jalali', 'Santanu Kolay', 'Peter Foldes', 'Ali Dasdan'] | 1305.3014v1 | Online advertising has been introduced as one of the most efficient methods
of advertising throughout the recent years. Yet, advertisers are concerned
about the efficiency of their online advertising campaigns and consequently,
would like to restrict their ad impressions to certain websites and/or certain
groups of audience. These restrictions, known as targeting criteria, limit the
reachability for better performance. This trade-off between reachability and
performance illustrates a need for a forecasting system that can quickly
predict/estimate (with good accuracy) this trade-off. Designing such a system
is challenging due to (a) the huge amount of data to process, and, (b) the need
for fast and accurate estimates. In this paper, we propose a distributed fault
tolerant system that can generate such estimates fast with good accuracy. The
main idea is to keep a small representative sample in memory across multiple
machines and formulate the forecasting problem as queries against the sample.
The key challenge is to find the best strata across the past data, perform
multivariate stratified sampling while ensuring fuzzy fall-back to cover the
small minorities. Our results show a significant improvement over the uniform
and simple stratified sampling strategies which are currently widely used in
the industry. | Scalable Audience Reach Estimation in Real-time Online Advertising | 2,013 | http://arxiv.org/pdf/1305.3014v1 | Title Scalable Audience Reach Estimation Realtime Online Advertising Summary Online advertising introduced one efficient method advertising throughout recent year Yet advertiser concerned efficiency online advertising campaign consequently would like restrict ad impression certain website andor certain group audience restriction known targeting criterion limit reachability better performance tradeoff reachability performance illustrates need forecasting system quickly predictestimate good accuracy tradeoff Designing system challenging due huge amount data process b need fast accurate estimate paper propose distributed fault tolerant system generate estimate fast good accuracy main idea keep small representative sample memory across multiple machine formulate forecasting problem query sample key challenge find best stratum across past data perform multivariate stratified sampling ensuring fuzzy fallback cover small minority result show significant improvement uniform simple stratified sampling strategy currently widely used industry Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.009138742461800575, 0.03396238014101982, -0.06712191551923752, -0.08605373650789261, -0.004468315280973911, -0.034344423562288284, 0.057514630258083344, -0.017312970012426376, 0.022039273753762245, -0.039002835750579834, -0.006297084968537092, -0.0870608389377594, -0.007051997818052769, 0.09217078983783722, 0.0017316469457000494, -0.03634866327047348, 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40,765 | 40,765 | ['Xiao-Bo Jin', 'Qiang Lu', 'Feng Wang', 'Quan-gong Huo'] | 1305.3149v1 | The study focused on the machine learning analysis approaches to identify the
adulteration of 9 kinds of edible oil qualitatively and answered the following
three questions: Is the oil sample adulterant? How does it constitute? What is
the main ingredient of the adulteration oil? After extracting the
high-performance liquid chromatography (HPLC) data on triglyceride from 370 oil
samples, we applied the adaptive boosting with multi-class Hamming loss
(AdaBoost.MH) to distinguish the oil adulteration in contrast with the support
vector machine (SVM). Further, we regarded the adulterant oil and the pure oil
samples as ones with multiple labels and with only one label, respectively.
Then multi-label AdaBoost.MH and multi-label learning vector quantization
(ML-LVQ) model were built to determine the ingredients and their relative ratio
in the adulteration oil. The experimental results on six measures show that
ML-LVQ achieves better performance than multi-label AdaBoost.MH. | Qualitative detection of oil adulteration with machine learning
approaches | 2,013 | http://arxiv.org/pdf/1305.3149v1 | Title Qualitative detection oil adulteration machine learning approach Summary study focused machine learning analysis approach identify adulteration 9 kind edible oil qualitatively answered following three question oil sample adulterant constitute main ingredient adulteration oil extracting highperformance liquid chromatography HPLC data triglyceride 370 oil sample applied adaptive boosting multiclass Hamming loss AdaBoostMH distinguish oil adulteration contrast support vector machine SVM regarded adulterant oil pure oil sample one multiple label one label respectively multilabel AdaBoostMH multilabel learning vector quantization MLLVQ model built determine ingredient relative ratio adulteration oil experimental result six measure show MLLVQ achieves better performance multilabel AdaBoostMH Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.03646443411707878, 0.035078227519989014, -0.04072501137852669, 0.018008684739470482, 0.008638706058263779, 0.021276872605085373, 0.008872238919138908, 0.05695314332842827, 0.004953477997332811, -0.03303569555282593, 0.05768534541130066, 0.058178361505270004, -0.02081873081624508, 0.045163754373788834, 0.0002848027506843209, 0.03661699593067169, -0.008853071369230747, 0.04415332153439522, 0.023577116429805756, 0.03365091234445572, 0.0017622008454054594, -0.020001288503408432, 0.03720392286777496, 0.01155827846378088, -0.0938611701130867, -0.024174774065613747, 0.004847508855164051, -0.06623946875333786, 0.024422183632850647, -0.04331280291080475, 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40,766 | 40,766 | ['Naseem Biadsy', 'Lior Rokach', 'Armin Shmilovici'] | 1305.3384v1 | In this paper we present a new approach to content-based transfer learning
for solving the data sparsity problem in cases when the users' preferences in
the target domain are either scarce or unavailable, but the necessary
information on the preferences exists in another domain. We show that training
a system to use such information across domains can produce better performance.
Specifically, we represent users' behavior patterns based on topological graph
structures. Each behavior pattern represents the behavior of a set of users,
when the users' behavior is defined as the items they rated and the items'
rating values. In the next step we find a correlation between behavior patterns
in the source domain and behavior patterns in the target domain. This mapping
is considered a bridge between the two domains. Based on the correlation and
content-attributes of the items, we train a machine learning model to predict
users' ratings in the target domain. When we compare our approach to the
popularity approach and KNN-cross-domain on a real world dataset, the results
show that on an average of 83$%$ of the cases our approach outperforms both
methods. | Transfer Learning for Content-Based Recommender Systems using Tree
Matching | 2,013 | http://arxiv.org/pdf/1305.3384v1 | Title Transfer Learning ContentBased Recommender Systems using Tree Matching Summary paper present new approach contentbased transfer learning solving data sparsity problem case user preference target domain either scarce unavailable necessary information preference exists another domain show training system use information across domain produce better performance Specifically represent user behavior pattern based topological graph structure behavior pattern represents behavior set user user behavior defined item rated item rating value next step find correlation behavior pattern source domain behavior pattern target domain mapping considered bridge two domain Based correlation contentattributes item train machine learning model predict user rating target domain compare approach popularity approach KNNcrossdomain real world dataset result show average 83 case approach outperforms method Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.0029111746698617935, 0.00988316535949707, -0.011506587266921997, 0.0053254286758601665, -0.01639598235487938, -0.024908769875764847, -0.004988569300621748, 0.014715210534632206, -0.0029665417969226837, -0.02801782265305519, -0.08394666761159897, 0.032944146543741226, -0.006981929764151573, 0.0881030485033989, -0.04768717288970947, -0.04063648357987404, -0.004742824472486973, 0.004918850027024746, -0.03220157325267792, 0.01046956330537796, 0.015823030844330788, 0.021866172552108765, 0.049074891954660416, -0.043102096766233444, 0.01574459671974182, 0.011164353229105473, 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40,767 | 40,767 | ['Ali Hadian', 'Behrouz Minaei-Bidgoli'] | 1305.3814v2 | Spam pages are designed to maliciously appear among the top search results by
excessive usage of popular terms. Therefore, spam pages should be removed using
an effective and efficient spam detection system. Previous methods for web spam
classification used several features from various information sources (page
contents, web graph, access logs, etc.) to detect web spam. In this paper, we
follow page-level classification approach to build fast and scalable spam
filters. We show that each web page can be classified with satisfiable accuracy
using only its own HTML content. In order to design a multi-view classification
system, we used state-of-the-art spam classification methods with distinct
feature sets (views) as the base classifiers. Then, a fusion model is learned
to combine the output of the base classifiers and make final prediction.
Results show that multi-view learning significantly improves the classification
performance, namely AUC by 22%, while providing linear speedup for parallel
execution. | Multi-View Learning for Web Spam Detection | 2,013 | http://arxiv.org/pdf/1305.3814v2 | Title MultiView Learning Web Spam Detection Summary Spam page designed maliciously appear among top search result excessive usage popular term Therefore spam page removed using effective efficient spam detection system Previous method web spam classification used several feature various information source page content web graph access log etc detect web spam paper follow pagelevel classification approach build fast scalable spam filter show web page classified satisfiable accuracy using HTML content order design multiview classification system used stateoftheart spam classification method distinct feature set view base classifier fusion model learned combine output base classifier make final prediction Results show multiview learning significantly improves classification performance namely AUC 22 providing linear speedup parallel execution Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.06253720074892044, -0.004458449315279722, -0.009293963201344013, 0.005665684584528208, -0.022415712475776672, 0.014188343659043312, 0.03610511124134064, 0.0177006796002388, 0.05633048713207245, -0.11937170475721359, -0.020006343722343445, -0.006146566942334175, 0.006031522061675787, 0.08362281322479248, -0.02418825775384903, -0.02140294574201107, -0.004529926925897598, 0.00542917987331748, 0.017125599086284637, 0.016934825107455254, 0.02894340269267559, -0.026589130982756615, 0.028935154899954796, 0.010837290436029434, -0.020249633118510246, -0.005497028585523367, 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40,768 | 40,768 | ['Michiaki Hamada', 'Hisanori Kiryu', 'Wataru Iwasaki', 'Kiyoshi Asai'] | 1305.4339v1 | In a number of estimation problems in bioinformatics, accuracy measures of
the target problem are usually given, and it is important to design estimators
that are suitable to those accuracy measures. However, there is often a
discrepancy between an employed estimator and a given accuracy measure of the
problem. In this study, we introduce a general class of efficient estimators
for estimation problems on high-dimensional binary spaces, which representmany
fundamental problems in bioinformatics. Theoretical analysis reveals that the
proposed estimators generally fit with commonly-used accuracy measures (e.g.
sensitivity, PPV, MCC and F-score) as well as it can be computed efficiently in
many cases, and cover a wide range of problems in bioinformatics from the
viewpoint of the principle of maximum expected accuracy (MEA). It is also shown
that some important algorithms in bioinformatics can be interpreted in a
unified manner. Not only the concept presented in this paper gives a useful
framework to design MEA-based estimators but also it is highly extendable and
sheds new light on many problems in bioinformatics. | Generalized Centroid Estimators in Bioinformatics | 2,013 | http://arxiv.org/pdf/1305.4339v1 | Title Generalized Centroid Estimators Bioinformatics Summary number estimation problem bioinformatics accuracy measure target problem usually given important design estimator suitable accuracy measure However often discrepancy employed estimator given accuracy measure problem study introduce general class efficient estimator estimation problem highdimensional binary space representmany fundamental problem bioinformatics Theoretical analysis reveals proposed estimator generally fit commonlyused accuracy measure eg sensitivity PPV MCC Fscore well computed efficiently many case cover wide range problem bioinformatics viewpoint principle maximum expected accuracy MEA also shown important algorithm bioinformatics interpreted unified manner concept presented paper give useful framework design MEAbased estimator also highly extendable shed new light many problem bioinformatics Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.011184494011104107, -0.044720593839883804, -0.04200432077050209, -0.040001221001148224, 0.0040039061568677425, -0.011553126387298107, 0.059868574142456055, 0.025659093633294106, -0.014324191026389599, 0.0012195418821647763, 0.031463973224163055, -0.028238333761692047, 0.05363796278834343, -0.0029338307213038206, -0.0017145158490166068, 0.027212288230657578, 0.0030205438379198313, 0.03991159796714783, 0.01681576482951641, -0.004491858184337616, 0.0038290300872176886, -0.0016596266068518162, 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40,769 | 40,769 | ['Miron B. Kursa'] | 1305.4525v3 | Gene selection is an important part of microarray data analysis because it
provides information that can lead to a better mechanistic understanding of an
investigated phenomenon. At the same time, gene selection is very difficult
because of the noisy nature of microarray data. As a consequence, gene
selection is often performed with machine learning methods. The Random Forest
method is particularly well suited for this purpose. In this work, four
state-of-the-art Random Forest-based feature selection methods were compared in
a gene selection context. The analysis focused on the stability of selection
because, although it is necessary for determining the significance of results,
it is often ignored in similar studies.
The comparison of post-selection accuracy in the validation of Random Forest
classifiers revealed that all investigated methods were equivalent in this
context. However, the methods substantially differed with respect to the number
of selected genes and the stability of selection. Of the analysed methods, the
Boruta algorithm predicted the most genes as potentially important.
The post-selection classifier error rate, which is a frequently used measure,
was found to be a potentially deceptive measure of gene selection quality. When
the number of consistently selected genes was considered, the Boruta algorithm
was clearly the best. Although it was also the most computationally intensive
method, the Boruta algorithm's computational demands could be reduced to levels
comparable to those of other algorithms by replacing the Random Forest
importance with a comparable measure from Random Ferns (a similar but
simplified classifier). Despite their design assumptions, the minimal optimal
selection methods, were found to select a high fraction of false positives. | Robustness of Random Forest-based gene selection methods | 2,013 | http://arxiv.org/pdf/1305.4525v3 | Title Robustness Random Forestbased gene selection method Summary Gene selection important part microarray data analysis provides information lead better mechanistic understanding investigated phenomenon time gene selection difficult noisy nature microarray data consequence gene selection often performed machine learning method Random Forest method particularly well suited purpose work four stateoftheart Random Forestbased feature selection method compared gene selection context analysis focused stability selection although necessary determining significance result often ignored similar study comparison postselection accuracy validation Random Forest classifier revealed investigated method equivalent context However method substantially differed respect number selected gene stability selection analysed method Boruta algorithm predicted gene potentially important postselection classifier error rate frequently used measure found potentially deceptive measure gene selection quality number consistently selected gene considered Boruta algorithm clearly best Although also computationally intensive method Boruta algorithm computational demand could reduced level comparable algorithm replacing Random Forest importance comparable measure Random Ferns similar simplified classifier Despite design assumption minimal optimal selection method found select high fraction false positive Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name 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40,770 | 40,770 | ['Parasaran Raman', 'Suresh Venkatasubramanian'] | 1305.4757v1 | A clustering is an implicit assignment of labels of points, based on
proximity to other points. It is these labels that are then used for downstream
analysis (either focusing on individual clusters, or identifying
representatives of clusters and so on). Thus, in order to trust a clustering as
a first step in exploratory data analysis, we must trust the labels assigned to
individual data. Without supervision, how can we validate this assignment? In
this paper, we present a method to attach affinity scores to the implicit
labels of individual points in a clustering. The affinity scores capture the
confidence level of the cluster that claims to "own" the point. This method is
very general: it can be used with clusterings derived from Euclidean data,
kernelized data, or even data derived from information spaces. It smoothly
incorporates importance functions on clusters, allowing us to eight different
clusters differently. It is also efficient: assigning an affinity score to a
point depends only polynomially on the number of clusters and is independent of
the number of points in the data. The dimensionality of the underlying space
only appears in preprocessing. We demonstrate the value of our approach with an
experimental study that illustrates the use of these scores in different data
analysis tasks, as well as the efficiency and flexibility of the method. We
also demonstrate useful visualizations of these scores; these might prove
useful within an interactive analytics framework. | Power to the Points: Validating Data Memberships in Clusterings | 2,013 | http://arxiv.org/pdf/1305.4757v1 | Title Power Points Validating Data Memberships Clusterings Summary clustering implicit assignment label point based proximity point label used downstream analysis either focusing individual cluster identifying representative cluster Thus order trust clustering first step exploratory data analysis must trust label assigned individual data Without supervision validate assignment paper present method attach affinity score implicit label individual point clustering affinity score capture confidence level cluster claim point method general used clustering derived Euclidean data kernelized data even data derived information space smoothly incorporates importance function cluster allowing u eight different cluster differently also efficient assigning affinity score point depends polynomially number cluster independent number point data dimensionality underlying space appears preprocessing demonstrate value approach experimental study illustrates use score different data analysis task well efficiency flexibility method also demonstrate useful visualization score might prove useful within interactive analytics framework Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.046151403337717056, -0.06476866453886032, -0.05189483240246773, -0.014674810692667961, 0.004007765091955662, -0.019697945564985275, 0.04081065580248833, 0.0012114556739106774, 0.055166807025671005, 0.026907937601208687, 0.0008642013999633491, 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40,771 | 40,771 | ['Bruno Ziliotto'] | 1305.4778v4 | Mertens [In Proceedings of the International Congress of Mathematicians
(Berkeley, Calif., 1986) (1987) 1528-1577 Amer. Math. Soc.] proposed two
general conjectures about repeated games: the first one is that, in any
two-person zero-sum repeated game, the asymptotic value exists, and the second
one is that, when Player 1 is more informed than Player 2, in the long run
Player 1 is able to guarantee the asymptotic value. We disprove these two
long-standing conjectures by providing an example of a zero-sum repeated game
with public signals and perfect observation of the actions, where the value of
the $\lambda$-discounted game does not converge when $\lambda$ goes to 0. The
aforementioned example involves seven states, two actions and two signals for
each player. Remarkably, players observe the payoffs, and play in turn. | Zero-sum repeated games: Counterexamples to the existence of the
asymptotic value and the conjecture
$\operatorname{maxmin}=\operatorname{lim}v_n$ | 2,013 | http://arxiv.org/pdf/1305.4778v4 | Title Zerosum repeated game Counterexamples existence asymptotic value conjecture operatornamemaxminoperatornamelimvn Summary Mertens Proceedings International Congress Mathematicians Berkeley Calif 1986 1987 15281577 Amer Math Soc proposed two general conjecture repeated game first one twoperson zerosum repeated game asymptotic value exists second one Player 1 informed Player 2 long run Player 1 able guarantee asymptotic value disprove two longstanding conjecture providing example zerosum repeated game public signal perfect observation action value lambdadiscounted game converge lambda go 0 aforementioned example involves seven state two action two signal player Remarkably player observe payoff play turn Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.003056693822145462, 0.018019024282693863, -0.017642347142100334, 0.02344895340502262, -0.0505065880715847, -0.06524236500263214, -0.02917751483619213, -0.005684110801666975, -0.022852320224046707, 0.020007511600852013, -0.029666759073734283, 0.029766511172056198, 0.00024765802663750947, 0.04485778510570526, 0.018478333950042725, -0.00733733968809247, 0.005168980918824673, 0.022499823942780495, 0.025591688230633736, -0.029748259112238884, 0.01062618475407362, -0.05102838948369026, -0.02689523994922638, -0.019213374704122543, 0.04664509370923042, -0.035654254257678986, -0.02394476905465126, -0.050142381340265274, 0.005333374720066786, -0.08519084006547928, -0.07495331764221191, -0.0019432601984590292, -0.009425518102943897, 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40,772 | 40,772 | ['Sidahmed Mokeddem', 'Baghdad Atmani', 'Mostefa Mokaddem'] | 1305.6046v1 | Feature Selection (FS) has become the focus of much research on decision
support systems areas for which data sets with tremendous number of variables
are analyzed. In this paper we present a new method for the diagnosis of
Coronary Artery Diseases (CAD) founded on Genetic Algorithm (GA) wrapped Bayes
Naive (BN) based FS. Basically, CAD dataset contains two classes defined with
13 features. In GA BN algorithm, GA generates in each iteration a subset of
attributes that will be evaluated using the BN in the second step of the
selection procedure. The final set of attribute contains the most relevant
feature model that increases the accuracy. The algorithm in this case produces
85.50% classification accuracy in the diagnosis of CAD. Thus, the asset of the
Algorithm is then compared with the use of Support Vector Machine (SVM),
MultiLayer Perceptron (MLP) and C4.5 decision tree Algorithm. The result of
classification accuracy for those algorithms are respectively 83.5%, 83.16% and
80.85%. Consequently, the GA wrapped BN Algorithm is correspondingly compared
with other FS algorithms. The Obtained results have shown very promising
outcomes for the diagnosis of CAD. | Supervised Feature Selection for Diagnosis of Coronary Artery Disease
Based on Genetic Algorithm | 2,013 | http://arxiv.org/pdf/1305.6046v1 | Title Supervised Feature Selection Diagnosis Coronary Artery Disease Based Genetic Algorithm Summary Feature Selection FS become focus much research decision support system area data set tremendous number variable analyzed paper present new method diagnosis Coronary Artery Diseases CAD founded Genetic Algorithm GA wrapped Bayes Naive BN based FS Basically CAD dataset contains two class defined 13 feature GA BN algorithm GA generates iteration subset attribute evaluated using BN second step selection procedure final set attribute contains relevant feature model increase accuracy algorithm case produce 8550 classification accuracy diagnosis CAD Thus asset Algorithm compared use Support Vector Machine SVM MultiLayer Perceptron MLP C45 decision tree Algorithm result classification accuracy algorithm respectively 835 8316 8085 Consequently GA wrapped BN Algorithm correspondingly compared FS algorithm Obtained result shown promising outcome diagnosis CAD Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.006475195754319429, 0.0025229083839803934, -0.028864536434412003, -0.025352058932185173, -0.021371880546212196, 0.020603595301508904, 0.021812979131937027, 0.06696011126041412, 0.03908208757638931, 0.010064211674034595, 0.09212013334035873, 0.02420506440103054, 0.046021170914173126, 0.03228612616658211, -0.06670135259628296, -0.026062818244099617, -0.040006399154663086, 0.027442514896392822, 0.0401039682328701, -0.023586483672261238, -0.006519809365272522, -0.009743395261466503, 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40,773 | 40,773 | ['Michael Zibulevsky'] | 1401.0159v1 | This is an overview paper written in style of research proposal. In recent
years we introduced a general framework for large-scale unconstrained
optimization -- Sequential Subspace Optimization (SESOP) and demonstrated its
usefulness for sparsity-based signal/image denoising, deconvolution,
compressive sensing, computed tomography, diffraction imaging, support vector
machines. We explored its combination with Parallel Coordinate Descent and
Separable Surrogate Function methods, obtaining state of the art results in
above-mentioned areas. There are several methods, that are faster than plain
SESOP under specific conditions: Trust region Newton method - for problems with
easily invertible Hessian matrix; Truncated Newton method - when fast
multiplication by Hessian is available; Stochastic optimization methods - for
problems with large stochastic-type data; Multigrid methods - for problems with
nested multilevel structure. Each of these methods can be further improved by
merge with SESOP. One can also accelerate Augmented Lagrangian method for
constrained optimization problems and Alternating Direction Method of
Multipliers for problems with separable objective function and non-separable
constraints. | Speeding-Up Convergence via Sequential Subspace Optimization: Current
State and Future Directions | 2,013 | http://arxiv.org/pdf/1401.0159v1 | Title SpeedingUp Convergence via Sequential Subspace Optimization Current State Future Directions Summary overview paper written style research proposal recent year introduced general framework largescale unconstrained optimization Sequential Subspace Optimization SESOP demonstrated usefulness sparsitybased signalimage denoising deconvolution compressive sensing computed tomography diffraction imaging support vector machine explored combination Parallel Coordinate Descent Separable Surrogate Function method obtaining state art result abovementioned area several method faster plain SESOP specific condition Trust region Newton method problem easily invertible Hessian matrix Truncated Newton method fast multiplication Hessian available Stochastic optimization method problem large stochastictype data Multigrid method problem nested multilevel structure method improved merge SESOP One also accelerate Augmented Lagrangian method constrained optimization problem Alternating Direction Method Multipliers problem separable objective function nonseparable constraint Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.028324943035840988, 0.06354746967554092, -0.025476286187767982, 0.034609194844961166, 0.009324838407337666, -0.004553230945020914, 0.03322033956646919, 0.02916734293103218, 0.014160479418933392, 0.04869028180837631, 0.023592771962285042, -0.010412495583295822, 0.05854830890893936, -0.0004943128442391753, -0.015675876289606094, -0.027023278176784515, -0.03624521940946579, 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40,774 | 40,774 | ['Maria-Florina Balcan', 'Yingyu Liang', 'Pramod Gupta'] | 1401.0247v2 | One of the most widely used techniques for data clustering is agglomerative
clustering. Such algorithms have been long used across many different fields
ranging from computational biology to social sciences to computer vision in
part because their output is easy to interpret. Unfortunately, it is well
known, however, that many of the classic agglomerative clustering algorithms
are not robust to noise. In this paper we propose and analyze a new robust
algorithm for bottom-up agglomerative clustering. We show that our algorithm
can be used to cluster accurately in cases where the data satisfies a number of
natural properties and where the traditional agglomerative algorithms fail. We
also show how to adapt our algorithm to the inductive setting where our given
data is only a small random sample of the entire data set. Experimental
evaluations on synthetic and real world data sets show that our algorithm
achieves better performance than other hierarchical algorithms in the presence
of noise. | Robust Hierarchical Clustering | 2,014 | http://arxiv.org/pdf/1401.0247v2 | Title Robust Hierarchical Clustering Summary One widely used technique data clustering agglomerative clustering algorithm long used across many different field ranging computational biology social science computer vision part output easy interpret Unfortunately well known however many classic agglomerative clustering algorithm robust noise paper propose analyze new robust algorithm bottomup agglomerative clustering show algorithm used cluster accurately case data satisfies number natural property traditional agglomerative algorithm fail also show adapt algorithm inductive setting given data small random sample entire data set Experimental evaluation synthetic real world data set show algorithm achieves better performance hierarchical algorithm presence noise Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.004384721163660288, 0.004667556378990412, -0.03758237510919571, 0.0011085752630606294, -0.017829494550824165, 0.010558963753283024, 0.020380958914756775, 0.012360576540231705, 0.0434715636074543, 0.0012754222843796015, 0.03288419172167778, 0.026324933394789696, 0.03362162783741951, 0.023188218474388123, 0.021036364138126373, -0.11160016804933548, 0.016670292243361473, -0.021607758477330208, -0.021552009508013725, -0.017746716737747192, 0.013867245055735111, -0.035593729466199875, 0.014215109869837761, -0.00628254096955061, 0.013587352819740772, -0.013268648646771908, -0.012205277569591999, -0.017687665298581123, 0.0359170101583004, -0.01564902439713478, 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40,775 | 40,775 | ['Dinesh Govindaraj', 'Tao Wang', 'S. V. N. Vishwanathan'] | 1401.0255v1 | Click models are an important tool for leveraging user feedback, and are used
by commercial search engines for surfacing relevant search results. However,
existing click models are lacking in two aspects. First, they do not share
information across search results when computing attractiveness. Second, they
assume that users interact with the search results sequentially. Based on our
analysis of the click logs of a commercial search engine, we observe that the
sequential scan assumption does not always hold, especially for sponsored
search results. To overcome the above two limitations, we propose a new click
model. Our key insight is that sharing information across search results helps
in identifying important words or key-phrases which can then be used to
accurately compute attractiveness of a search result. Furthermore, we argue
that the click probability of a position as well as its attractiveness changes
during a user session and depends on the user's past click experience. Our
model seamlessly incorporates the effect of externalities (quality of other
search results displayed in response to a user query), user fatigue, as well as
pre and post-click relevance of a sponsored search result. We propose an
efficient one-pass inference scheme and empirically evaluate the performance of
our model via extensive experiments using the click logs of a large commercial
search engine. | Modeling Attractiveness and Multiple Clicks in Sponsored Search Results | 2,014 | http://arxiv.org/pdf/1401.0255v1 | Title Modeling Attractiveness Multiple Clicks Sponsored Search Results Summary Click model important tool leveraging user feedback used commercial search engine surfacing relevant search result However existing click model lacking two aspect First share information across search result computing attractiveness Second assume user interact search result sequentially Based analysis click log commercial search engine observe sequential scan assumption always hold especially sponsored search result overcome two limitation propose new click model key insight sharing information across search result help identifying important word keyphrases used accurately compute attractiveness search result Furthermore argue click probability position well attractiveness change user session depends user past click experience model seamlessly incorporates effect externality quality search result displayed response user query user fatigue well pre postclick relevance sponsored search result propose efficient onepass inference scheme empirically evaluate performance model via extensive experiment using click log large commercial search engine Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.055399090051651, 0.008134845644235611, -0.017781510949134827, -0.012884088791906834, -0.023470312356948853, -0.02051403746008873, 0.010080951265990734, -0.0012429385678842664, 0.022764386609196663, -0.07197141647338867, -0.06257651001214981, -0.016187194734811783, 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40,776 | 40,776 | ['Warren R. Scott', 'Warren B. Powell', 'Somayeh Moazehi'] | 1401.0843v1 | This paper studies approximate policy iteration (API) methods which use
least-squares Bellman error minimization for policy evaluation. We address
several of its enhancements, namely, Bellman error minimization using
instrumental variables, least-squares projected Bellman error minimization, and
projected Bellman error minimization using instrumental variables. We prove
that for a general discrete-time stochastic control problem, Bellman error
minimization using instrumental variables is equivalent to both variants of
projected Bellman error minimization. An alternative to these API methods is
direct policy search based on knowledge gradient. The practical performance of
these three approximate dynamic programming methods are then investigated in
the context of an application in energy storage, integrated with an
intermittent wind energy supply to fully serve a stochastic time-varying
electricity demand. We create a library of test problems using real-world data
and apply value iteration to find their optimal policies. These benchmarks are
then used to compare the developed policies. Our analysis indicates that API
with instrumental variables Bellman error minimization prominently outperforms
API with least-squares Bellman error minimization. However, these approaches
underperform our direct policy search implementation. | Least Squares Policy Iteration with Instrumental Variables vs. Direct
Policy Search: Comparison Against Optimal Benchmarks Using Energy Storage | 2,014 | http://arxiv.org/pdf/1401.0843v1 | Title Least Squares Policy Iteration Instrumental Variables v Direct Policy Search Comparison Optimal Benchmarks Using Energy Storage Summary paper study approximate policy iteration API method use leastsquares Bellman error minimization policy evaluation address several enhancement namely Bellman error minimization using instrumental variable leastsquares projected Bellman error minimization projected Bellman error minimization using instrumental variable prove general discretetime stochastic control problem Bellman error minimization using instrumental variable equivalent variant projected Bellman error minimization alternative API method direct policy search based knowledge gradient practical performance three approximate dynamic programming method investigated context application energy storage integrated intermittent wind energy supply fully serve stochastic timevarying electricity demand create library test problem using realworld data apply value iteration find optimal policy benchmark used compare developed policy analysis indicates API instrumental variable Bellman error minimization prominently outperforms API leastsquares Bellman error minimization However approach underperform direct policy search implementation Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.0039984420873224735, 0.04244862496852875, -0.006562928669154644, 0.00914664939045906, 0.042264848947525024, -0.019429760053753853, 0.017078934237360954, 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40,777 | 40,777 | ['Pokkuluri Kiran Sree', 'Inamupudi Ramesh Babu', 'SSSN Usha Devi N'] | 1401.2688v1 | Protein Structure Predication from sequences of amino acid has gained a
remarkable attention in recent years. Even though there are some prediction
techniques addressing this problem, the approximate accuracy in predicting the
protein structure is closely 75%. An automated procedure was evolved with MACA
(Multiple Attractor Cellular Automata) for predicting the structure of the
protein. Most of the existing approaches are sequential which will classify the
input into four major classes and these are designed for similar sequences.
PSMACA is designed to identify ten classes from the sequences that share
twilight zone similarity and identity with the training sequences. This method
also predicts three states (helix, strand, and coil) for the structure. Our
comprehensive design considers 10 feature selection methods and 4 classifiers
to develop MACA (Multiple Attractor Cellular Automata) based classifiers that
are build for each of the ten classes. We have tested the proposed classifier
with twilight-zone and 1-high-similarity benchmark datasets with over three
dozens of modern competing predictors shows that PSMACA provides the best
overall accuracy that ranges between 77% and 88.7% depending on the dataset. | PSMACA: An Automated Protein Structure Prediction Using MACA (Multiple
Attractor Cellular Automata) | 2,014 | http://arxiv.org/pdf/1401.2688v1 | Title PSMACA Automated Protein Structure Prediction Using MACA Multiple Attractor Cellular Automata Summary Protein Structure Predication sequence amino acid gained remarkable attention recent year Even though prediction technique addressing problem approximate accuracy predicting protein structure closely 75 automated procedure evolved MACA Multiple Attractor Cellular Automata predicting structure protein existing approach sequential classify input four major class designed similar sequence PSMACA designed identify ten class sequence share twilight zone similarity identity training sequence method also predicts three state helix strand coil structure comprehensive design considers 10 feature selection method 4 classifier develop MACA Multiple Attractor Cellular Automata based classifier build ten class tested proposed classifier twilightzone 1highsimilarity benchmark datasets three dozen modern competing predictor show PSMACA provides best overall accuracy range 77 887 depending dataset Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.0014317762106657028, 0.016116423532366753, -0.027939321473240852, -0.011080785654485226, 0.012876896187663078, 0.010826369747519493, -0.03626824915409088, -0.025493016466498375, -0.03710602596402168, -0.006687600165605545, 0.013573091477155685, -0.01904298923909664, 0.026855414733290672, 0.007283470127731562, 0.002846757648512721, 0.03175617754459381, 0.011132288724184036, 0.015338540077209473, -0.027716869488358498, -0.008810160681605339, 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40,778 | 40,778 | ['Shashank Mouli Satapathy', 'Santanu Kumar Rath'] | 1401.3069v2 | The job of software effort estimation is a critical one in the early stages
of the software development life cycle when the details of requirements are
usually not clearly identified. Various optimization techniques help in
improving the accuracy of effort estimation. The Support Vector Regression
(SVR) is one of several different soft-computing techniques that help in
getting optimal estimated values. The idea of SVR is based upon the computation
of a linear regression function in a high dimensional feature space where the
input data are mapped via a nonlinear function. Further, the SVR kernel methods
can be applied in transforming the input data and then based on these
transformations, an optimal boundary between the possible outputs can be
obtained. The main objective of the research work carried out in this paper is
to estimate the software effort using use case point approach. The use case
point approach relies on the use case diagram to estimate the size and effort
of software projects. Then, an attempt has been made to optimize the results
obtained from use case point analysis using various SVR kernel methods to
achieve better prediction accuracy. | Use Case Point Approach Based Software Effort Estimation using Various
Support Vector Regression Kernel Methods | 2,014 | http://arxiv.org/pdf/1401.3069v2 | Title Use Case Point Approach Based Software Effort Estimation using Various Support Vector Regression Kernel Methods Summary job software effort estimation critical one early stage software development life cycle detail requirement usually clearly identified Various optimization technique help improving accuracy effort estimation Support Vector Regression SVR one several different softcomputing technique help getting optimal estimated value idea SVR based upon computation linear regression function high dimensional feature space input data mapped via nonlinear function SVR kernel method applied transforming input data based transformation optimal boundary possible output obtained main objective research work carried paper estimate software effort using use case point approach use case point approach relies use case diagram estimate size effort software project attempt made optimize result obtained use case point analysis using various SVR kernel method achieve better prediction accuracy Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.012590484693646431, -0.023131627589464188, -0.0407552495598793, -0.010899391025304794, 0.018552672117948532, -0.030821558088064194, 0.020167527720332146, -0.03657178580760956, -0.04494677111506462, 0.0005672212573699653, 0.08279842138290405, 0.07278797775506973, 0.03578558564186096, 0.08667786419391632, 0.0019338533747941256, -0.012614639475941658, 0.01904628798365593, 0.03971017897129059, -0.016590630635619164, 0.03731308877468109, 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40,779 | 40,779 | ['Avneesh Saluja', 'Mahdi Pakdaman', 'Dongzhen Piao', 'Ankur P. Parikh'] | 1401.3413v1 | Rating and recommendation systems have become a popular application area for
applying a suite of machine learning techniques. Current approaches rely
primarily on probabilistic interpretations and extensions of matrix
factorization, which factorizes a user-item ratings matrix into latent user and
item vectors. Most of these methods fail to model significant variations in
item ratings from otherwise similar users, a phenomenon known as the "Napoleon
Dynamite" effect. Recent efforts have addressed this problem by adding a
contextual bias term to the rating, which captures the mood under which a user
rates an item or the context in which an item is rated by a user. In this work,
we extend this model in a nonparametric sense by learning the optimal number of
moods or contexts from the data, and derive Gibbs sampling inference procedures
for our model. We evaluate our approach on the MovieLens 1M dataset, and show
significant improvements over the optimal parametric baseline, more than twice
the improvements previously encountered for this task. We also extract and
evaluate a DBLP dataset, wherein we predict the number of papers co-authored by
two authors, and present improvements over the parametric baseline on this
alternative domain as well. | Infinite Mixed Membership Matrix Factorization | 2,014 | http://arxiv.org/pdf/1401.3413v1 | Title Infinite Mixed Membership Matrix Factorization Summary Rating recommendation system become popular application area applying suite machine learning technique Current approach rely primarily probabilistic interpretation extension matrix factorization factorizes useritem rating matrix latent user item vector method fail model significant variation item rating otherwise similar user phenomenon known Napoleon Dynamite effect Recent effort addressed problem adding contextual bias term rating capture mood user rate item context item rated user work extend model nonparametric sense learning optimal number mood context data derive Gibbs sampling inference procedure model evaluate approach MovieLens 1M dataset show significant improvement optimal parametric baseline twice improvement previously encountered task also extract evaluate DBLP dataset wherein predict number paper coauthored two author present improvement parametric baseline alternative domain well Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.014655216597020626, 0.0016639274545013905, -0.011736350134015083, 0.00809055007994175, 0.022580573335289955, 0.023030588403344154, 0.018819713965058327, 0.0345759391784668, -0.019156472757458687, -0.004833172541111708, -0.08143507689237595, 0.012684092856943607, -0.00602626521140337, 0.09456238895654678, -0.03284021466970444, -0.021754013374447823, -0.0027352648321539164, 0.005983121693134308, -0.019220614805817604, -0.007262476719915867, 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40,780 | 40,780 | ['Sherief Abdallah', 'Victor Lesser'] | 1401.3454v1 | Several multiagent reinforcement learning (MARL) algorithms have been
proposed to optimize agents decisions. Due to the complexity of the problem,
the majority of the previously developed MARL algorithms assumed agents either
had some knowledge of the underlying game (such as Nash equilibria) and/or
observed other agents actions and the rewards they received.
We introduce a new MARL algorithm called the Weighted Policy Learner (WPL),
which allows agents to reach a Nash Equilibrium (NE) in benchmark
2-player-2-action games with minimum knowledge. Using WPL, the only feedback an
agent needs is its own local reward (the agent does not observe other agents
actions or rewards). Furthermore, WPL does not assume that agents know the
underlying game or the corresponding Nash Equilibrium a priori. We
experimentally show that our algorithm converges in benchmark
two-player-two-action games. We also show that our algorithm converges in the
challenging Shapleys game where previous MARL algorithms failed to converge
without knowing the underlying game or the NE. Furthermore, we show that WPL
outperforms the state-of-the-art algorithms in a more realistic setting of 100
agents interacting and learning concurrently.
An important aspect of understanding the behavior of a MARL algorithm is
analyzing the dynamics of the algorithm: how the policies of multiple learning
agents evolve over time as agents interact with one another. Such an analysis
not only verifies whether agents using a given MARL algorithm will eventually
converge, but also reveals the behavior of the MARL algorithm prior to
convergence. We analyze our algorithm in two-player-two-action games and show
that symbolically proving WPLs convergence is difficult, because of the
non-linear nature of WPLs dynamics, unlike previous MARL algorithms that had
either linear or piece-wise-linear dynamics. Instead, we numerically solve WPLs
dynamics differential equations and compare the solution to the dynamics of
previous MARL algorithms. | A Multiagent Reinforcement Learning Algorithm with Non-linear Dynamics | 2,014 | http://arxiv.org/pdf/1401.3454v1 | Title Multiagent Reinforcement Learning Algorithm Nonlinear Dynamics Summary Several multiagent reinforcement learning MARL algorithm proposed optimize agent decision Due complexity problem majority previously developed MARL algorithm assumed agent either knowledge underlying game Nash equilibrium andor observed agent action reward received introduce new MARL algorithm called Weighted Policy Learner WPL allows agent reach Nash Equilibrium NE benchmark 2player2action game minimum knowledge Using WPL feedback agent need local reward agent observe agent action reward Furthermore WPL assume agent know underlying game corresponding Nash Equilibrium priori experimentally show algorithm converges benchmark twoplayertwoaction game also show algorithm converges challenging Shapleys game previous MARL algorithm failed converge without knowing underlying game NE Furthermore show WPL outperforms stateoftheart algorithm realistic setting 100 agent interacting learning concurrently important aspect understanding behavior MARL algorithm analyzing dynamic algorithm policy multiple learning agent evolve time agent interact one another analysis verifies whether agent using given MARL algorithm eventually converge also reveals behavior MARL algorithm prior convergence analyze algorithm twoplayertwoaction game show symbolically proving WPLs convergence difficult nonlinear nature WPLs dynamic unlike previous MARL algorithm either linear piecewiselinear dynamic Instead numerically solve WPLs dynamic differential equation compare solution dynamic previous MARL algorithm Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.024061108008027077, -0.028882518410682678, -0.014037826098501682, -0.06513488292694092, -0.020318476483225822, -0.005304432939738035, -0.0013908455148339272, -0.00627412274479866, -0.02276814915239811, 0.015004480257630348, -0.006902511231601238, 0.07255431264638901, -0.011540861800312996, 0.0267686415463686, 0.015984268859028816, -0.02694348245859146, 0.002252191537991166, -0.0027703503146767616, 0.027171403169631958, -0.0472819060087204, 0.03228128328919411, -0.07396673411130905, -0.00425854604691267, 0.010664794594049454, -0.002610849915072322, -0.008745224215090275, -0.04827044531702995, -0.026277689263224602, 0.07073258608579636, -0.06824218481779099, 0.05410779267549515, -0.03157385438680649, 0.0007427059463225305, 0.04064347967505455, 2.2937026642466662e-06, -0.037407036870718, 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40,781 | 40,781 | ['Amy Greenwald', 'Seong Jae Lee', 'Victor Naroditskiy'] | 1401.3829v1 | In this paper, we describe our autonomous bidding agent, RoxyBot, who emerged
victorious in the travel division of the 2006 Trading Agent Competition in a
photo finish. At a high level, the design of many successful trading agents can
be summarized as follows: (i) price prediction: build a model of market prices;
and (ii) optimization: solve for an approximately optimal set of bids, given
this model. To predict, RoxyBot builds a stochastic model of market prices by
simulating simultaneous ascending auctions. To optimize, RoxyBot relies on the
sample average approximation method, a stochastic optimization technique. | RoxyBot-06: Stochastic Prediction and Optimization in TAC Travel | 2,014 | http://arxiv.org/pdf/1401.3829v1 | Title RoxyBot06 Stochastic Prediction Optimization TAC Travel Summary paper describe autonomous bidding agent RoxyBot emerged victorious travel division 2006 Trading Agent Competition photo finish high level design many successful trading agent summarized follows price prediction build model market price ii optimization solve approximately optimal set bid given model predict RoxyBot build stochastic model market price simulating simultaneous ascending auction optimize RoxyBot relies sample average approximation method stochastic optimization technique Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.03918810933828354, 0.013473479077219963, -0.02647332474589348, -0.043266378343105316, -0.012083487585186958, -0.0480644665658474, 0.019761336967349052, -0.004079832695424557, -0.015351835638284683, -0.01641896367073059, 0.010832998901605606, 0.019161591306328773, -0.02233816869556904, 0.12434093654155731, 0.013785884715616703, -0.06895984709262848, 0.0005869246670044959, -0.036242932081222534, -0.00243561458773911, -0.01740119606256485, 0.028123972937464714, -0.03518879413604736, -0.003729684744030237, 0.05097242072224617, 0.008560719899833202, -0.025964245200157166, -0.07887361943721771, -0.017884736880660057, 0.016161849722266197, -0.08736900985240936, 0.03375551849603653, 0.0022144916001707315, 0.025311918929219246, 0.0420660637319088, 2.1865164399059722e-06, -0.02038407139480114, 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40,782 | 40,782 | ['Liyue Zhao', 'Yu Zhang', 'Gita Sukthankar'] | 1401.3836v1 | Crowdsourcing platforms offer a practical solution to the problem of
affordably annotating large datasets for training supervised classifiers.
Unfortunately, poor worker performance frequently threatens to compromise
annotation reliability, and requesting multiple labels for every instance can
lead to large cost increases without guaranteeing good results. Minimizing the
required training samples using an active learning selection procedure reduces
the labeling requirement but can jeopardize classifier training by focusing on
erroneous annotations. This paper presents an active learning approach in which
worker performance, task difficulty, and annotation reliability are jointly
estimated and used to compute the risk function guiding the sample selection
procedure. We demonstrate that the proposed approach, which employs active
learning with Bayesian networks, significantly improves training accuracy and
correctly ranks the expertise of unknown labelers in the presence of annotation
noise. | An Active Learning Approach for Jointly Estimating Worker Performance
and Annotation Reliability with Crowdsourced Data | 2,014 | http://arxiv.org/pdf/1401.3836v1 | Title Active Learning Approach Jointly Estimating Worker Performance Annotation Reliability Crowdsourced Data Summary Crowdsourcing platform offer practical solution problem affordably annotating large datasets training supervised classifier Unfortunately poor worker performance frequently threatens compromise annotation reliability requesting multiple label every instance lead large cost increase without guaranteeing good result Minimizing required training sample using active learning selection procedure reduces labeling requirement jeopardize classifier training focusing erroneous annotation paper present active learning approach worker performance task difficulty annotation reliability jointly estimated used compute risk function guiding sample selection procedure demonstrate proposed approach employ active learning Bayesian network significantly improves training accuracy correctly rank expertise unknown labelers presence annotation noise Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.0055186389945447445, 0.03031683713197708, -0.018285177648067474, -0.020148154348134995, 0.020585743710398674, 0.01776966266334057, 0.06732286512851715, 0.009181655012071133, -0.009655785746872425, -0.04213211312890053, 0.0195729099214077, 0.051923397928476334, -0.012409679591655731, 0.02449650876224041, 0.03389641270041466, -0.03932153433561325, -0.0002563952293712646, 0.0025877212174236774, 0.029751241207122803, 0.02488325908780098, -0.0009710932499729097, 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40,783 | 40,783 | ['Xiaosong Lu', 'Howard M. Schwartz', 'Sidney N. Givigi Jr'] | 1401.3907v1 | We extend the potential-based shaping method from Markov decision processes
to multi-player general-sum stochastic games. We prove that the Nash equilibria
in a stochastic game remains unchanged after potential-based shaping is applied
to the environment. The property of policy invariance provides a possible way
of speeding convergence when learning to play a stochastic game. | Policy Invariance under Reward Transformations for General-Sum
Stochastic Games | 2,014 | http://arxiv.org/pdf/1401.3907v1 | Title Policy Invariance Reward Transformations GeneralSum Stochastic Games Summary extend potentialbased shaping method Markov decision process multiplayer generalsum stochastic game prove Nash equilibrium stochastic game remains unchanged potentialbased shaping applied environment property policy invariance provides possible way speeding convergence learning play stochastic game Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.012330411933362484, 0.010303500108420849, -0.007802112493664026, -0.0580420196056366, 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40,784 | 40,784 | ['Charles-Henri Cappelaere', 'R. Dubois', 'P. Roussel', 'G. Dreyfus'] | 1401.4128v1 | The purpose of this study is to optimize the selection of prophylactic
cardioverter defibrillator implantation candidates. Currently, the main
criterion for implantation is a low Left Ventricular Ejection Fraction (LVEF)
whose specificity is relatively poor. We designed two classifiers aimed to
predict, from long term ECG recordings (Holter), whether a low-LVEF patient is
likely or not to undergo ventricular arrhythmia in the next six months. One
classifier is a single hidden layer neural network whose variables are the most
relevant features extracted from Holter recordings, and the other classifier
has a structure that capitalizes on the physiological decomposition of the
arrhythmogenic factors into three disjoint groups: the myocardial substrate,
the triggers and the autonomic nervous system (ANS). In this ad hoc network,
the features were assigned to each group; one neural network classifier per
group was designed and its complexity was optimized. The outputs of the
classifiers were fed to a single neuron that provided the required probability
estimate. The latter was thresholded for final discrimination A dataset
composed of 186 pre-implantation 30-mn Holter recordings of patients equipped
with an implantable cardioverter defibrillator (ICD) in primary prevention was
used in order to design and test this classifier. 44 out of 186 patients
underwent at least one treated ventricular arrhythmia during the six-month
follow-up period. Performances of the designed classifier were evaluated using
a cross-test strategy that consists in splitting the database into several
combinations of a training set and a test set. The average arrhythmia
prediction performances of the ad-hoc classifier are NPV = 77% $\pm$ 13% and
PPV = 31% $\pm$ 19% (Negative Predictive Value $\pm$ std, Positive Predictive
Value $\pm$ std). According to our study, improving prophylactic
ICD-implantation candidate selection by automatic classification from ECG
features may be possible, but the availability of a sizable dataset appears to
be essential to decrease the number of False Negatives. | Towards the selection of patients requiring ICD implantation by
automatic classification from Holter monitoring indices | 2,014 | http://arxiv.org/pdf/1401.4128v1 | Title Towards selection patient requiring ICD implantation automatic classification Holter monitoring index Summary purpose study optimize selection prophylactic cardioverter defibrillator implantation candidate Currently main criterion implantation low Left Ventricular Ejection Fraction LVEF whose specificity relatively poor designed two classifier aimed predict long term ECG recording Holter whether lowLVEF patient likely undergo ventricular arrhythmia next six month One classifier single hidden layer neural network whose variable relevant feature extracted Holter recording classifier structure capitalizes physiological decomposition arrhythmogenic factor three disjoint group myocardial substrate trigger autonomic nervous system ANS ad hoc network feature assigned group one neural network classifier per group designed complexity optimized output classifier fed single neuron provided required probability estimate latter thresholded final discrimination dataset composed 186 preimplantation 30mn Holter recording patient equipped implantable cardioverter defibrillator ICD primary prevention used order design test classifier 44 186 patient underwent least one treated ventricular arrhythmia sixmonth followup period Performances designed classifier evaluated using crosstest strategy consists splitting database several combination training set test set average arrhythmia prediction performance adhoc classifier NPV 77 pm 13 PPV 31 pm 19 Negative Predictive Value pm std Positive Predictive Value pm std According study improving prophylactic ICDimplantation candidate selection automatic classification ECG feature may possible availability sizable dataset appears essential decrease number False Negatives Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.018920773640275, -0.03850593790411949, -0.023619642481207848, -0.0048650456592440605, 0.04041318967938423, 0.018475960940122604, 0.05051766708493233, -0.006629271898418665, 0.017325278371572495, 0.024147698655724525, 0.04160416126251221, -0.02008049748837948, 0.04759438335895538, 0.02280777134001255, -0.008409538306295872, 0.004089453257620335, 0.05952171981334686, 0.026035292074084282, 0.04439439997076988, -0.0029143423307687044, -0.027311263605952263, -0.021770566701889038, -0.0047125243581831455, 0.08020329475402832, 0.02471119351685047, 0.013346499763429165, 0.010197862982749939, -0.037071697413921356, 0.007849542424082756, -0.07468574494123459, 0.025632483884692192, 0.00957061629742384, 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40,785 | 40,785 | ['Balázs Hidasi', 'Domonkos Tikk'] | 1401.4529v2 | Context-aware recommendation algorithms focus on refining recommendations by
considering additional information, available to the system. This topic has
gained a lot of attention recently. Among others, several factorization methods
were proposed to solve the problem, although most of them assume explicit
feedback which strongly limits their real-world applicability. While these
algorithms apply various loss functions and optimization strategies, the
preference modeling under context is less explored due to the lack of tools
allowing for easy experimentation with various models. As context dimensions
are introduced beyond users and items, the space of possible preference models
and the importance of proper modeling largely increases.
In this paper we propose a General Factorization Framework (GFF), a single
flexible algorithm that takes the preference model as an input and computes
latent feature matrices for the input dimensions. GFF allows us to easily
experiment with various linear models on any context-aware recommendation task,
be it explicit or implicit feedback based. The scaling properties makes it
usable under real life circumstances as well.
We demonstrate the framework's potential by exploring various preference
models on a 4-dimensional context-aware problem with contexts that are
available for almost any real life datasets. We show in our experiments --
performed on five real life, implicit feedback datasets -- that proper
preference modelling significantly increases recommendation accuracy, and
previously unused models outperform the traditional ones. Novel models in GFF
also outperform state-of-the-art factorization algorithms.
We also extend the method to be fully compliant to the Multidimensional
Dataspace Model, one of the most extensive data models of context-enriched
data. Extended GFF allows the seamless incorporation of information into the
fac[truncated] | General factorization framework for context-aware recommendations | 2,014 | http://arxiv.org/pdf/1401.4529v2 | Title General factorization framework contextaware recommendation Summary Contextaware recommendation algorithm focus refining recommendation considering additional information available system topic gained lot attention recently Among others several factorization method proposed solve problem although assume explicit feedback strongly limit realworld applicability algorithm apply various loss function optimization strategy preference modeling context le explored due lack tool allowing easy experimentation various model context dimension introduced beyond user item space possible preference model importance proper modeling largely increase paper propose General Factorization Framework GFF single flexible algorithm take preference model input computes latent feature matrix input dimension GFF allows u easily experiment various linear model contextaware recommendation task explicit implicit feedback based scaling property make usable real life circumstance well demonstrate framework potential exploring various preference model 4dimensional contextaware problem context available almost real life datasets show experiment performed five real life implicit feedback datasets proper preference modelling significantly increase recommendation accuracy previously unused model outperform traditional one Novel model GFF also outperform stateoftheart factorization algorithm also extend method fully compliant Multidimensional Dataspace Model one extensive data model contextenriched data Extended GFF allows seamless incorporation information factruncated Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.009666632860898972, -0.008485202677547932, 0.0020051696337759495, -0.005266155581921339, 0.029174929484725, -0.013981504365801811, 0.05003528669476509, -0.0018511617090553045, -0.02739035151898861, -0.01568659581243992, -0.04538194090127945, 0.045614175498485565, -0.0036004800349473953, 0.0650300458073616, -0.021410400047898293, -0.051231175661087036, 0.005769600160419941, 0.007895821705460548, -0.021778428927063942, 0.020227482542395592, 0.04573184996843338, -0.003706214250996709, 0.051422398537397385, 0.025872137397527695, 0.009403523057699203, 0.012547416612505913, 0.0006042821332812309, -0.021490532904863358, 0.015267019160091877, -0.030098481103777885, 0.032962046563625336, 0.012729461304843426, 0.006493501830846071, -0.04633685201406479, 2.619044380480773e-06, -0.003840360790491104, -0.034109629690647125, 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40,786 | 40,786 | ['Rania Ibrahim', 'Noha A. Yousri', 'Mohamed A. Ismail', 'Nagwa M. El-Makky'] | 1401.4589v1 | miRNA and gene expression profiles have been proved useful for classifying
cancer samples. Efficient classifiers have been recently sought and developed.
A number of attempts to classify cancer samples using miRNA/gene expression
profiles are known in literature. However, the use of semi-supervised learning
models have been used recently in bioinformatics, to exploit the huge corpuses
of publicly available sets. Using both labeled and unlabeled sets to train
sample classifiers, have not been previously considered when gene and miRNA
expression sets are used. Moreover, there is a motivation to integrate both
miRNA and gene expression for a semi-supervised cancer classification as that
provides more information on the characteristics of cancer samples. In this
paper, two semi-supervised machine learning approaches, namely self-learning
and co-training, are adapted to enhance the quality of cancer sample
classification. These approaches exploit the huge public corpuses to enrich the
training data. In self-learning, miRNA and gene based classifiers are enhanced
independently. While in co-training, both miRNA and gene expression profiles
are used simultaneously to provide different views of cancer samples. To our
knowledge, it is the first attempt to apply these learning approaches to cancer
classification. The approaches were evaluated using breast cancer,
hepatocellular carcinoma (HCC) and lung cancer expression sets. Results show up
to 20% improvement in F1-measure over Random Forests and SVM classifiers.
Co-Training also outperforms Low Density Separation (LDS) approach by around
25% improvement in F1-measure in breast cancer. | miRNA and Gene Expression based Cancer Classification using Self-
Learning and Co-Training Approaches | 2,014 | http://arxiv.org/pdf/1401.4589v1 | Title miRNA Gene Expression based Cancer Classification using Self Learning CoTraining Approaches Summary miRNA gene expression profile proved useful classifying cancer sample Efficient classifier recently sought developed number attempt classify cancer sample using miRNAgene expression profile known literature However use semisupervised learning model used recently bioinformatics exploit huge corpus publicly available set Using labeled unlabeled set train sample classifier previously considered gene miRNA expression set used Moreover motivation integrate miRNA gene expression semisupervised cancer classification provides information characteristic cancer sample paper two semisupervised machine learning approach namely selflearning cotraining adapted enhance quality cancer sample classification approach exploit huge public corpus enrich training data selflearning miRNA gene based classifier enhanced independently cotraining miRNA gene expression profile used simultaneously provide different view cancer sample knowledge first attempt apply learning approach cancer classification approach evaluated using breast cancer hepatocellular carcinoma HCC lung cancer expression set Results show 20 improvement F1measure Random Forests SVM classifier CoTraining also outperforms Low Density Separation LDS approach around 25 improvement F1measure breast cancer Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.0010452247224748135, 0.023351024836301804, -0.030806604772806168, 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40,787 | 40,787 | ['Pokkuluri Kiran Sree', 'Inampudi Ramesh Babu', 'SSSN Usha Devi N'] | 1401.5364v1 | Human body consists of lot of cells, each cell consist of DeOxaRibo Nucleic
Acid (DNA). Identifying the genes from the DNA sequences is a very difficult
task. But identifying the coding regions is more complex task compared to the
former. Identifying the protein which occupy little place in genes is a really
challenging issue. For understating the genes coding region analysis plays an
important role. Proteins are molecules with macro structure that are
responsible for a wide range of vital biochemical functions, which includes
acting as oxygen, cell signaling, antibody production, nutrient transport and
building up muscle fibers. Promoter region identification and protein structure
prediction has gained a remarkable attention in recent years. Even though there
are some identification techniques addressing this problem, the approximate
accuracy in identifying the promoter region is closely 68% to 72%. We have
developed a Cellular Automata based tool build with hybrid multiple attractor
cellular automata (HMACA) classifier for protein coding region, promoter region
identification and protein structure prediction which predicts the protein and
promoter regions with an accuracy of 76%. This tool also predicts the structure
of protein with an accuracy of 80%. | HMACA: Towards Proposing a Cellular Automata Based Tool for Protein
Coding, Promoter Region Identification and Protein Structure Prediction | 2,014 | http://arxiv.org/pdf/1401.5364v1 | Title HMACA Towards Proposing Cellular Automata Based Tool Protein Coding Promoter Region Identification Protein Structure Prediction Summary Human body consists lot cell cell consist DeOxaRibo Nucleic Acid DNA Identifying gene DNA sequence difficult task identifying coding region complex task compared former Identifying protein occupy little place gene really challenging issue understating gene coding region analysis play important role Proteins molecule macro structure responsible wide range vital biochemical function includes acting oxygen cell signaling antibody production nutrient transport building muscle fiber Promoter region identification protein structure prediction gained remarkable attention recent year Even though identification technique addressing problem approximate accuracy identifying promoter region closely 68 72 developed Cellular Automata based tool build hybrid multiple attractor cellular automaton HMACA classifier protein coding region promoter region identification protein structure prediction predicts protein promoter region accuracy 76 tool also predicts structure protein accuracy 80 Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.031957730650901794, 0.0217808336019516, -0.04391604661941528, -0.0220172218978405, 0.027868978679180145, 0.003802444553002715, 0.003526165382936597, 0.006573591381311417, -0.02960420772433281, -0.014143751934170723, 0.001721972250379622, 0.02788086049258709, 0.01721847429871559, 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40,788 | 40,788 | ['Cyril Voyant', 'Gilles Notton', 'Christophe Paoli', 'Marie Laure Nivet', 'Marc Muselli', 'Kahina Dahmani'] | 1401.6002v1 | Numerous methods exist and were developed for global radiation forecasting.
The two most popular types are the numerical weather predictions (NWP) and the
predictions using stochastic approaches. We propose to compute a parameter
noted constructed in part from the mutual information which is a quantity that
measures the mutual dependence of two variables. Both of these are calculated
with the objective to establish the more relevant method between NWP and
stochastic models concerning the current problem. | Numerical weather prediction or stochastic modeling: an objective
criterion of choice for the global radiation forecasting | 2,014 | http://arxiv.org/pdf/1401.6002v1 | Title Numerical weather prediction stochastic modeling objective criterion choice global radiation forecasting Summary Numerous method exist developed global radiation forecasting two popular type numerical weather prediction NWP prediction using stochastic approach propose compute parameter noted constructed part mutual information quantity measure mutual dependence two variable calculated objective establish relevant method NWP stochastic model concerning current problem Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed 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40,789 | 40,789 | ['Fabricio Olivetti de Franca'] | 1401.6124v1 | Minhashing is a technique used to estimate the Jaccard Index between two sets
by exploiting the probability of collision in a random permutation. In order to
speed up the computation, a random permutation can be approximated by using an
universal hash function such as the $h_{a,b}$ function proposed by Carter and
Wegman. A better estimate of the Jaccard Index can be achieved by using many of
these hash functions, created at random. In this paper a new iterative
procedure to generate a set of $h_{a,b}$ functions is devised that eliminates
the need for a list of random values and avoid the multiplication operation
during the calculation. The properties of the generated hash functions remains
that of an universal hash function family. This is possible due to the random
nature of features occurrence on sparse datasets. Results show that the
uniformity of hashing the features is maintaned while obtaining a speed up of
up to $1.38$ compared to the traditional approach. | Iterative Universal Hash Function Generator for Minhashing | 2,014 | http://arxiv.org/pdf/1401.6124v1 | Title Iterative Universal Hash Function Generator Minhashing Summary Minhashing technique used estimate Jaccard Index two set exploiting probability collision random permutation order speed computation random permutation approximated using universal hash function hab function proposed Carter Wegman better estimate Jaccard Index achieved using many hash function created random paper new iterative procedure generate set hab function devised eliminates need list random value avoid multiplication operation calculation property generated hash function remains universal hash function family possible due random nature feature occurrence sparse datasets Results show uniformity hashing feature maintaned obtaining speed 138 compared traditional approach Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.017153043299913406, -0.013598740100860596, -0.015307098627090454, -0.0011090096086263657, -0.03292590752243996, -0.013295156881213188, 0.050779037177562714, -0.017339713871479034, 0.01943398453295231, 0.017019256949424744, 0.05691045522689819, -0.00020305856014601886, -0.034871067851781845, 0.028119690716266632, -0.012870445847511292, -0.013364813290536404, 0.010769383050501347, 0.015744879841804504, -0.05884675309062004, -0.02163584902882576, -0.028191938996315002, -0.03686392679810524, -0.009410644881427288, 0.003184756962582469, -0.011294838972389698, 0.015256363898515701, -0.054798416793346405, 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40,790 | 40,790 | ['Pokkuluri Kiran Sree', 'Inampudi Ramesh Babu'] | 1401.6484v1 | Genes carry the instructions for making proteins that are found in a cell as
a specific sequence of nucleotides that are found in DNA molecules. But, the
regions of these genes that code for proteins may occupy only a small region of
the sequence. Identifying the coding regions play a vital role in understanding
these genes. In this paper we propose a unsupervised Fuzzy Multiple Attractor
Cellular Automata (FMCA) based pattern classifier to identify the coding region
of a DNA sequence. We propose a distinct K-Means algorithm for designing FMACA
classifier which is simple, efficient and produces more accurate classifier
than that has previously been obtained for a range of different sequence
lengths. Experimental results confirm the scalability of the proposed
Unsupervised FCA based classifier to handle large volume of datasets
irrespective of the number of classes, tuples and attributes. Good
classification accuracy has been established. | Identification of Protein Coding Regions in Genomic DNA Using
Unsupervised FMACA Based Pattern Classifier | 2,014 | http://arxiv.org/pdf/1401.6484v1 | Title Identification Protein Coding Regions Genomic DNA Using Unsupervised FMACA Based Pattern Classifier Summary Genes carry instruction making protein found cell specific sequence nucleotide found DNA molecule region gene code protein may occupy small region sequence Identifying coding region play vital role understanding gene paper propose unsupervised Fuzzy Multiple Attractor Cellular Automata FMCA based pattern classifier identify coding region DNA sequence propose distinct KMeans algorithm designing FMACA classifier simple efficient produce accurate classifier previously obtained range different sequence length Experimental result confirm scalability proposed Unsupervised FCA based classifier handle large volume datasets irrespective number class tuples attribute Good classification accuracy established Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.05465226620435715, 0.05571216717362404, -0.06234829127788544, -0.003486165078356862, 0.03847436234354973, 0.027177754789590836, -0.0007753277313895524, 0.020194489508867264, -0.019965719431638718, -0.014994173310697079, 0.024750888347625732, -0.009197360835969448, 0.04018043726682663, 0.046285487711429596, 0.001864256919361651, 0.05058780312538147, -0.026413360610604286, 0.027583330869674683, 0.008839666843414307, 0.0024648322723805904, -0.0023556454107165337, -0.022160572931170464, 0.03276006877422333, 0.03126521408557892, -0.009416540153324604, 0.021467668935656548, 0.00045611875248141587, -0.08070899546146393, 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40,791 | 40,791 | ['Battista Biggio', 'Igino Corona', 'Blaine Nelson', 'Benjamin I. P. Rubinstein', 'Davide Maiorca', 'Giorgio Fumera', 'Giorgio Giacinto', 'and Fabio Roli'] | 1401.7727v1 | Support Vector Machines (SVMs) are among the most popular classification
techniques adopted in security applications like malware detection, intrusion
detection, and spam filtering. However, if SVMs are to be incorporated in
real-world security systems, they must be able to cope with attack patterns
that can either mislead the learning algorithm (poisoning), evade detection
(evasion), or gain information about their internal parameters (privacy
breaches). The main contributions of this chapter are twofold. First, we
introduce a formal general framework for the empirical evaluation of the
security of machine-learning systems. Second, according to our framework, we
demonstrate the feasibility of evasion, poisoning and privacy attacks against
SVMs in real-world security problems. For each attack technique, we evaluate
its impact and discuss whether (and how) it can be countered through an
adversary-aware design of SVMs. Our experiments are easily reproducible thanks
to open-source code that we have made available, together with all the employed
datasets, on a public repository. | Security Evaluation of Support Vector Machines in Adversarial
Environments | 2,014 | http://arxiv.org/pdf/1401.7727v1 | Title Security Evaluation Support Vector Machines Adversarial Environments Summary Support Vector Machines SVMs among popular classification technique adopted security application like malware detection intrusion detection spam filtering However SVMs incorporated realworld security system must able cope attack pattern either mislead learning algorithm poisoning evade detection evasion gain information internal parameter privacy breach main contribution chapter twofold First introduce formal general framework empirical evaluation security machinelearning system Second according framework demonstrate feasibility evasion poisoning privacy attack SVMs realworld security problem attack technique evaluate impact discus whether countered adversaryaware design SVMs experiment easily reproducible thanks opensource code made available together employed datasets public repository Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.0460398904979229, -0.01719612069427967, -0.02032344415783882, 0.013870248571038246, -0.03908965736627579, -0.02961774170398712, 0.051121536642313004, -0.024792801588773727, 0.04373655095696449, -0.04008975625038147, 0.03824464976787567, 0.023007331416010857, 0.011043652892112732, 0.06383705139160156, 0.036202821880578995, -0.01119237206876278, 0.04973535239696503, -0.01955147460103035, -0.0211455337703228, -0.010598821565508842, 0.023138485848903656, -0.06131142005324364, 0.017853407189249992, 0.029563557356595993, -0.00362554588355124, -0.009621858596801758, 0.01284446194767952, 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40,792 | 40,792 | ['Erik Zawadzki', 'Asher Lipson', 'Kevin Leyton-Brown'] | 1401.8074v1 | There exist many algorithms for learning how to play repeated bimatrix games.
Most of these algorithms are justified in terms of some sort of theoretical
guarantee. On the other hand, little is known about the empirical performance
of these algorithms. Most such claims in the literature are based on small
experiments, which has hampered understanding as well as the development of new
multiagent learning (MAL) algorithms. We have developed a new suite of tools
for running multiagent experiments: the MultiAgent Learning Testbed (MALT).
These tools are designed to facilitate larger and more comprehensive
experiments by removing the need to build one-off experimental code. MALT also
provides baseline implementations of many MAL algorithms, hopefully eliminating
or reducing differences between algorithm implementations and increasing the
reproducibility of results. Using this test suite, we ran an experiment
unprecedented in size. We analyzed the results according to a variety of
performance metrics including reward, maxmin distance, regret, and several
notions of equilibrium convergence. We confirmed several pieces of conventional
wisdom, but also discovered some surprising results. For example, we found that
single-agent $Q$-learning outperformed many more complicated and more modern
MAL algorithms. | Empirically Evaluating Multiagent Learning Algorithms | 2,014 | http://arxiv.org/pdf/1401.8074v1 | Title Empirically Evaluating Multiagent Learning Algorithms Summary exist many algorithm learning play repeated bimatrix game algorithm justified term sort theoretical guarantee hand little known empirical performance algorithm claim literature based small experiment hampered understanding well development new multiagent learning MAL algorithm developed new suite tool running multiagent experiment MultiAgent Learning Testbed MALT tool designed facilitate larger comprehensive experiment removing need build oneoff experimental code MALT also provides baseline implementation many MAL algorithm hopefully eliminating reducing difference algorithm implementation increasing reproducibility result Using test suite ran experiment unprecedented size analyzed result according variety performance metric including reward maxmin distance regret several notion equilibrium convergence confirmed several piece conventional wisdom also discovered surprising result example found singleagent Qlearning outperformed many complicated modern MAL algorithm Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.02561907097697258, 0.008090117946267128, -0.011793098412454128, -0.029700275510549545, -0.04857203736901283, -0.00784502737224102, 0.06821363419294357, 0.02435232698917389, 0.023859048262238503, -0.02665098011493683, -0.015852587297558784, 0.03792216256260872, -0.010031639598309994, 0.025827016681432724, 0.024074766784906387, -0.0009752702317200601, 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40,793 | 40,793 | ['Franziska Meier', 'Philipp Hennig', 'Stefan Schaal'] | 1402.0645v1 | Locally weighted regression was created as a nonparametric learning method
that is computationally efficient, can learn from very large amounts of data
and add data incrementally. An interesting feature of locally weighted
regression is that it can work with spatially varying length scales, a
beneficial property, for instance, in control problems. However, it does not
provide a generative model for function values and requires training and test
data to be generated identically, independently. Gaussian (process) regression,
on the other hand, provides a fully generative model without significant formal
requirements on the distribution of training data, but has much higher
computational cost and usually works with one global scale per input dimension.
Using a localising function basis and approximate inference techniques, we take
Gaussian (process) regression to increasingly localised properties and toward
the same computational complexity class as locally weighted regression. | Local Gaussian Regression | 2,014 | http://arxiv.org/pdf/1402.0645v1 | Title Local Gaussian Regression Summary Locally weighted regression created nonparametric learning method computationally efficient learn large amount data add data incrementally interesting feature locally weighted regression work spatially varying length scale beneficial property instance control problem However provide generative model function value requires training test data generated identically independently Gaussian process regression hand provides fully generative model without significant formal requirement distribution training data much higher computational cost usually work one global scale per input dimension Using localising function basis approximate inference technique take Gaussian process regression increasingly localised property toward computational complexity class locally weighted regression Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.01887545734643936, 0.07433664798736572, -0.015480962581932545, -0.029647961258888245, -0.01330823265016079, -0.0075213722884655, 0.09184826910495758, -0.0017460115486755967, -0.0018838683608919382, 0.0835656225681305, 0.04742762818932533, 0.020412437617778778, 0.017645392566919327, 0.051600996404886246, 0.05107661709189415, -0.07298216223716736, -0.060823336243629456, -0.005205994937568903, -0.0125704575330019, -0.057623241096735, 0.008270224556326866, -0.02573641575872898, 0.014429192990064621, 0.041086580604314804, -0.09808438271284103, 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40,794 | 40,794 | ['Eli A. Meirom', 'Chris Milling', 'Constantine Caramanis', 'Shie Mannor', 'Ariel Orda', 'Sanjay Shakkottai'] | 1402.1263v1 | We consider the problem of detecting an epidemic in a population where
individual diagnoses are extremely noisy. The motivation for this problem is
the plethora of examples (influenza strains in humans, or computer viruses in
smartphones, etc.) where reliable diagnoses are scarce, but noisy data
plentiful. In flu/phone-viruses, exceedingly few infected people/phones are
professionally diagnosed (only a small fraction go to a doctor) but less
reliable secondary signatures (e.g., people staying home, or
greater-than-typical upload activity) are more readily available. These
secondary data are often plagued by unreliability: many people with the flu do
not stay home, and many people that stay home do not have the flu. This paper
identifies the precise regime where knowledge of the contact network enables
finding the needle in the haystack: we provide a distributed, efficient and
robust algorithm that can correctly identify the existence of a spreading
epidemic from highly unreliable local data. Our algorithm requires only
local-neighbor knowledge of this graph, and in a broad array of settings that
we describe, succeeds even when false negatives and false positives make up an
overwhelming fraction of the data available. Our results show it succeeds in
the presence of partial information about the contact network, and also when
there is not a single "patient zero", but rather many (hundreds, in our
examples) of initial patient-zeroes, spread across the graph. | Localized epidemic detection in networks with overwhelming noise | 2,014 | http://arxiv.org/pdf/1402.1263v1 | Title Localized epidemic detection network overwhelming noise Summary consider problem detecting epidemic population individual diagnosis extremely noisy motivation problem plethora example influenza strain human computer virus smartphones etc reliable diagnosis scarce noisy data plentiful fluphoneviruses exceedingly infected peoplephones professionally diagnosed small fraction go doctor le reliable secondary signature eg people staying home greaterthantypical upload activity readily available secondary data often plagued unreliability many people flu stay home many people stay home flu paper identifies precise regime knowledge contact network enables finding needle haystack provide distributed efficient robust algorithm correctly identify existence spreading epidemic highly unreliable local data algorithm requires localneighbor knowledge graph broad array setting describe succeeds even false negative false positive make overwhelming fraction data available result show succeeds presence partial information contact network also single patient zero rather many hundred example initial patientzeroes spread across graph Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.015425793826580048, 0.03522845730185509, -0.01739526353776455, -0.036642443388700485, -0.008390109054744244, -0.0565761923789978, -0.013179666362702847, -0.0271783284842968, 0.028146598488092422, 0.046591367572546005, 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40,795 | 40,795 | ['Jianshu Chen', 'Zaid J. Towfic', 'Ali H. Sayed'] | 1402.1515v2 | In this paper, we consider learning dictionary models over a network of
agents, where each agent is only in charge of a portion of the dictionary
elements. This formulation is relevant in Big Data scenarios where large
dictionary models may be spread over different spatial locations and it is not
feasible to aggregate all dictionaries in one location due to communication and
privacy considerations. We first show that the dual function of the inference
problem is an aggregation of individual cost functions associated with
different agents, which can then be minimized efficiently by means of diffusion
strategies. The collaborative inference step generates dual variables that are
used by the agents to update their dictionaries without the need to share these
dictionaries or even the coefficient models for the training data. This is a
powerful property that leads to an effective distributed procedure for learning
dictionaries over large networks (e.g., hundreds of agents in our experiments).
Furthermore, the proposed learning strategy operates in an online manner and is
able to respond to streaming data, where each data sample is presented to the
network once. | Dictionary Learning over Distributed Models | 2,014 | http://arxiv.org/pdf/1402.1515v2 | Title Dictionary Learning Distributed Models Summary paper consider learning dictionary model network agent agent charge portion dictionary element formulation relevant Big Data scenario large dictionary model may spread different spatial location feasible aggregate dictionary one location due communication privacy consideration first show dual function inference problem aggregation individual cost function associated different agent minimized efficiently mean diffusion strategy collaborative inference step generates dual variable used agent update dictionary without need share dictionary even coefficient model training data powerful property lead effective distributed procedure learning dictionary large network eg hundred agent experiment Furthermore proposed learning strategy operates online manner able respond streaming data data sample presented network Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.002968002110719681, 0.020892061293125153, -0.0005802790401503444, -0.01473424956202507, -0.03160270303487778, -0.000884791137650609, 0.07631000876426697, -0.042370811104774475, 0.021008070558309555, -0.0012869936181232333, 0.025067340582609177, 0.033188141882419586, -0.012636867351830006, 0.07105343788862228, 0.00440658675506711, -0.01903190277516842, 0.038553591817617416, -0.01621013693511486, -0.03675655648112297, -0.04199056327342987, 0.0043122838251292706, -0.022319432348012924, 0.01467378344386816, 0.011490550823509693, -0.021864542737603188, 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40,796 | 40,796 | ['Amos Beimel', 'Kobbi Nissim', 'Uri Stemmer'] | 1402.2224v1 | In 2008, Kasiviswanathan et al. defined private learning as a combination of
PAC learning and differential privacy. Informally, a private learner is applied
to a collection of labeled individual information and outputs a hypothesis
while preserving the privacy of each individual. Kasiviswanathan et al. gave a
generic construction of private learners for (finite) concept classes, with
sample complexity logarithmic in the size of the concept class. This sample
complexity is higher than what is needed for non-private learners, hence
leaving open the possibility that the sample complexity of private learning may
be sometimes significantly higher than that of non-private learning.
We give a combinatorial characterization of the sample size sufficient and
necessary to privately learn a class of concepts. This characterization is
analogous to the well known characterization of the sample complexity of
non-private learning in terms of the VC dimension of the concept class. We
introduce the notion of probabilistic representation of a concept class, and
our new complexity measure RepDim corresponds to the size of the smallest
probabilistic representation of the concept class.
We show that any private learning algorithm for a concept class C with sample
complexity m implies RepDim(C)=O(m), and that there exists a private learning
algorithm with sample complexity m=O(RepDim(C)). We further demonstrate that a
similar characterization holds for the database size needed for privately
computing a large class of optimization problems and also for the well studied
problem of private data release. | Characterizing the Sample Complexity of Private Learners | 2,014 | http://arxiv.org/pdf/1402.2224v1 | Title Characterizing Sample Complexity Private Learners Summary 2008 Kasiviswanathan et al defined private learning combination PAC learning differential privacy Informally private learner applied collection labeled individual information output hypothesis preserving privacy individual Kasiviswanathan et al gave generic construction private learner finite concept class sample complexity logarithmic size concept class sample complexity higher needed nonprivate learner hence leaving open possibility sample complexity private learning may sometimes significantly higher nonprivate learning give combinatorial characterization sample size sufficient necessary privately learn class concept characterization analogous well known characterization sample complexity nonprivate learning term VC dimension concept class introduce notion probabilistic representation concept class new complexity measure RepDim corresponds size smallest probabilistic representation concept class show private learning algorithm concept class C sample complexity implies RepDimCOm exists private learning algorithm sample complexity mORepDimC demonstrate similar characterization hold database size needed privately computing large class optimization problem also well studied problem private data release Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.03026667982339859, 0.06445037573575974, -0.026514515280723572, -1.121978675655555e-05, -0.047428715974092484, 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40,797 | 40,797 | ['Moritz Hardt', 'Raghu Meka', 'Prasad Raghavendra', 'Benjamin Weitz'] | 1402.2331v2 | Matrix Completion is the problem of recovering an unknown real-valued
low-rank matrix from a subsample of its entries. Important recent results show
that the problem can be solved efficiently under the assumption that the
unknown matrix is incoherent and the subsample is drawn uniformly at random.
Are these assumptions necessary?
It is well known that Matrix Completion in its full generality is NP-hard.
However, little is known if make additional assumptions such as incoherence and
permit the algorithm to output a matrix of slightly higher rank. In this paper
we prove that Matrix Completion remains computationally intractable even if the
unknown matrix has rank $4$ but we are allowed to output any constant rank
matrix, and even if additionally we assume that the unknown matrix is
incoherent and are shown $90%$ of the entries. This result relies on the
conjectured hardness of the $4$-Coloring problem. We also consider the positive
semidefinite Matrix Completion problem. Here we show a similar hardness result
under the standard assumption that $\mathrm{P}\ne \mathrm{NP}.$
Our results greatly narrow the gap between existing feasibility results and
computational lower bounds. In particular, we believe that our results give the
first complexity-theoretic justification for why distributional assumptions are
needed beyond the incoherence assumption in order to obtain positive results.
On the technical side, we contribute several new ideas on how to encode hard
combinatorial problems in low-rank optimization problems. We hope that these
techniques will be helpful in further understanding the computational limits of
Matrix Completion and related problems. | Computational Limits for Matrix Completion | 2,014 | http://arxiv.org/pdf/1402.2331v2 | Title Computational Limits Matrix Completion Summary Matrix Completion problem recovering unknown realvalued lowrank matrix subsample entry Important recent result show problem solved efficiently assumption unknown matrix incoherent subsample drawn uniformly random assumption necessary well known Matrix Completion full generality NPhard However little known make additional assumption incoherence permit algorithm output matrix slightly higher rank paper prove Matrix Completion remains computationally intractable even unknown matrix rank 4 allowed output constant rank matrix even additionally assume unknown matrix incoherent shown 90 entry result relies conjectured hardness 4Coloring problem also consider positive semidefinite Matrix Completion problem show similar hardness result standard assumption mathrmPne mathrmNP result greatly narrow gap existing feasibility result computational lower bound particular believe result give first complexitytheoretic justification distributional assumption needed beyond incoherence assumption order obtain positive result technical side contribute several new idea encode hard combinatorial problem lowrank optimization problem hope technique helpful understanding computational limit Matrix Completion related problem Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [0.016797494143247604, 0.08306434005498886, -0.007595382165163755, 0.02152487263083458, 0.016870666295289993, 0.003042366122826934, 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40,798 | 40,798 | ['P. Chaudhari', 'D. P. Rana', 'R. G. Mehta', 'N. J. Mistry', 'M. M. Raghuwanshi'] | 1402.4283v1 | In real world, the huge amount of temporal data is to be processed in many
application areas such as scientific, financial, network monitoring, sensor
data analysis. Data mining techniques are primarily oriented to handle discrete
features. In the case of temporal data the time plays an important role on the
characteristics of data. To consider this effect, the data discretization
techniques have to consider the time while processing to resolve the issue by
finding the intervals of data which are more concise and precise with respect
to time. Here, this research is reviewing different data discretization
techniques used in temporal data applications according to the inclusion or
exclusion of: class label, temporal order of the data and handling of stream
data to open the research direction for temporal data discretization to improve
the performance of data mining technique. | Discretization of Temporal Data: A Survey | 2,014 | http://arxiv.org/pdf/1402.4283v1 | Title Discretization Temporal Data Survey Summary real world huge amount temporal data processed many application area scientific financial network monitoring sensor data analysis Data mining technique primarily oriented handle discrete feature case temporal data time play important role characteristic data consider effect data discretization technique consider time processing resolve issue finding interval data concise precise respect time research reviewing different data discretization technique used temporal data application according inclusion exclusion class label temporal order data handling stream data open research direction temporal data discretization improve performance data mining technique Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | [-0.03848325461149216, 0.03579120337963104, -0.04701657220721245, -0.035535190254449844, 0.007648705039173365, -0.017788996919989586, 0.012478316202759743, -0.0019220283720642328, 0.005435439758002758, 0.020121721550822258, 0.09435232728719711, 0.004387122578918934, 0.011880740523338318, 0.07541532814502716, -0.07104256004095078, -0.05252792686223984, 0.0614389143884182, 0.012085944414138794, 0.022057827562093735, 0.04698136821389198, 0.013634075410664082, -0.03526608645915985, -0.003837122116237879, 0.008833942003548145, 0.028746668249368668, -0.014200057834386826, 0.009121224284172058, -0.061420414596796036, 0.04516689106822014, -0.0729406476020813, 0.08213583379983902, 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40,799 | 40,799 | ['Jonathan Gelati', 'Sithan Kanna'] | 1402.4845v1 | In this technical report we analyse the performance of diffusion strategies
applied to the Least-Mean-Square adaptive filter. We configure a network of
cooperative agents running adaptive filters and discuss their behaviour when
compared with a non-cooperative agent which represents the average of the
network. The analysis provides conditions under which diversity in the filter
parameters is beneficial in terms of convergence and stability. Simulations
drive and support the analysis. | Diffusion Least Mean Square: Simulations | 2,014 | http://arxiv.org/pdf/1402.4845v1 | Title Diffusion Least Mean Square Simulations Summary technical report analyse performance diffusion strategy applied LeastMeanSquare adaptive filter configure network cooperative agent running adaptive filter discus behaviour compared noncooperative agent represents average network analysis provides condition diversity filter parameter beneficial term convergence stability Simulations drive support analysis Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object | 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Subsets and Splits