categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
list |
|---|---|---|---|---|---|---|---|---|---|---|
cs.LG | null | 1404.7472 | null | null | http://arxiv.org/pdf/1404.7472v1 | 2014-04-29T19:28:09Z | 2014-04-29T19:28:09Z | Implementing spectral methods for hidden Markov models with real-valued
emissions | Hidden Markov models (HMMs) are widely used statistical models for modeling
sequential data. The parameter estimation for HMMs from time series data is an
important learning problem. The predominant methods for parameter estimation
are based on local search heuristics, most notably the expectation-maximization
(EM) algorithm. These methods are prone to local optima and oftentimes suffer
from high computational and sample complexity. Recent years saw the emergence
of spectral methods for the parameter estimation of HMMs, based on a method of
moments approach. Two spectral learning algorithms as proposed by Hsu, Kakade
and Zhang 2012 (arXiv:0811.4413) and Anandkumar, Hsu and Kakade 2012
(arXiv:1203.0683) are assessed in this work. Using experiments with synthetic
data, the algorithms are compared with each other. Furthermore, the spectral
methods are compared to the Baum-Welch algorithm, a well-established method
applying the EM algorithm to HMMs. The spectral algorithms are found to have a
much more favorable computational and sample complexity. Even though the
algorithms readily handle high dimensional observation spaces, instability
issues are encountered in this regime. In view of learning from real-world
experimental data, the representation of real-valued observations for the use
in spectral methods is discussed, presenting possible methods to represent data
for the use in the learning algorithms.
| [
"Carl Mattfeld",
"['Carl Mattfeld']"
]
|
cs.LG | null | 1404.7527 | null | null | http://arxiv.org/pdf/1404.7527v2 | 2014-07-03T21:46:43Z | 2014-04-29T20:54:40Z | A Map of Update Constraints in Inductive Inference | We investigate how different learning restrictions reduce learning power and
how the different restrictions relate to one another. We give a complete map
for nine different restrictions both for the cases of complete information
learning and set-driven learning. This completes the picture for these
well-studied \emph{delayable} learning restrictions. A further insight is
gained by different characterizations of \emph{conservative} learning in terms
of variants of \emph{cautious} learning.
Our analyses greatly benefit from general theorems we give, for example
showing that learners with exclusively delayable restrictions can always be
assumed total.
| [
"['Timo Kötzing' 'Raphaela Palenta']",
"Timo K\\\"otzing and Raphaela Palenta"
]
|
stat.ML cs.LG cs.MM | null | 1404.7796 | null | null | http://arxiv.org/pdf/1404.7796v2 | 2014-06-19T08:06:24Z | 2014-04-30T16:55:00Z | Majority Vote of Diverse Classifiers for Late Fusion | In the past few years, a lot of attention has been devoted to multimedia
indexing by fusing multimodal informations. Two kinds of fusion schemes are
generally considered: The early fusion and the late fusion. We focus on late
classifier fusion, where one combines the scores of each modality at the
decision level. To tackle this problem, we investigate a recent and elegant
well-founded quadratic program named MinCq coming from the machine learning
PAC-Bayesian theory. MinCq looks for the weighted combination, over a set of
real-valued functions seen as voters, leading to the lowest misclassification
rate, while maximizing the voters' diversity. We propose an extension of MinCq
tailored to multimedia indexing. Our method is based on an order-preserving
pairwise loss adapted to ranking that allows us to improve Mean Averaged
Precision measure while taking into account the diversity of the voters that we
want to fuse. We provide evidence that this method is naturally adapted to late
fusion procedures and confirm the good behavior of our approach on the
challenging PASCAL VOC'07 benchmark.
| [
"['Emilie Morvant' 'Amaury Habrard' 'Stéphane Ayache']",
"Emilie Morvant (IST Austria), Amaury Habrard (LHC), St\\'ephane Ayache\n (LIF)"
]
|
cs.NE cs.LG | 10.1016/j.neunet.2014.09.003 | 1404.7828 | null | null | http://arxiv.org/abs/1404.7828v4 | 2014-10-08T10:00:38Z | 2014-04-30T18:39:00Z | Deep Learning in Neural Networks: An Overview | In recent years, deep artificial neural networks (including recurrent ones)
have won numerous contests in pattern recognition and machine learning. This
historical survey compactly summarises relevant work, much of it from the
previous millennium. Shallow and deep learners are distinguished by the depth
of their credit assignment paths, which are chains of possibly learnable,
causal links between actions and effects. I review deep supervised learning
(also recapitulating the history of backpropagation), unsupervised learning,
reinforcement learning & evolutionary computation, and indirect search for
short programs encoding deep and large networks.
| [
"['Juergen Schmidhuber']",
"Juergen Schmidhuber"
]
|
stat.ML cs.LG math.OC math.PR | null | 1405.0042 | null | null | http://arxiv.org/pdf/1405.0042v2 | 2015-06-15T13:12:12Z | 2014-04-30T21:48:34Z | Learning with incremental iterative regularization | Within a statistical learning setting, we propose and study an iterative
regularization algorithm for least squares defined by an incremental gradient
method. In particular, we show that, if all other parameters are fixed a
priori, the number of passes over the data (epochs) acts as a regularization
parameter, and prove strong universal consistency, i.e. almost sure convergence
of the risk, as well as sharp finite sample bounds for the iterates. Our
results are a step towards understanding the effect of multiple epochs in
stochastic gradient techniques in machine learning and rely on integrating
statistical and optimization results.
| [
"['Lorenzo Rosasco' 'Silvia Villa']",
"Lorenzo Rosasco, Silvia Villa"
]
|
stat.ML cs.LG | null | 1405.0099 | null | null | null | null | null | Fast MLE Computation for the Dirichlet Multinomial | Given a collection of categorical data, we want to find the parameters of a
Dirichlet distribution which maximizes the likelihood of that data. Newton's
method is typically used for this purpose but current implementations require
reading through the entire dataset on each iteration. In this paper, we propose
a modification which requires only a single pass through the dataset and
substantially decreases running time. Furthermore we analyze both theoretically
and empirically the performance of the proposed algorithm, and provide an open
source implementation.
| [
"Max Sklar"
]
|
cs.LG math.DG stat.ML | null | 1405.0133 | null | null | http://arxiv.org/pdf/1405.0133v2 | 2014-05-08T05:07:21Z | 2014-05-01T11:10:36Z | Geodesic Distance Function Learning via Heat Flow on Vector Fields | Learning a distance function or metric on a given data manifold is of great
importance in machine learning and pattern recognition. Many of the previous
works first embed the manifold to Euclidean space and then learn the distance
function. However, such a scheme might not faithfully preserve the distance
function if the original manifold is not Euclidean. Note that the distance
function on a manifold can always be well-defined. In this paper, we propose to
learn the distance function directly on the manifold without embedding. We
first provide a theoretical characterization of the distance function by its
gradient field. Based on our theoretical analysis, we propose to first learn
the gradient field of the distance function and then learn the distance
function itself. Specifically, we set the gradient field of a local distance
function as an initial vector field. Then we transport it to the whole manifold
via heat flow on vector fields. Finally, the geodesic distance function can be
obtained by requiring its gradient field to be close to the normalized vector
field. Experimental results on both synthetic and real data demonstrate the
effectiveness of our proposed algorithm.
| [
"['Binbin Lin' 'Ji Yang' 'Xiaofei He' 'Jieping Ye']",
"Binbin Lin, Ji Yang, Xiaofei He and Jieping Ye"
]
|
cs.LG cs.AI | null | 1405.0501 | null | null | http://arxiv.org/pdf/1405.0501v1 | 2014-05-02T20:13:06Z | 2014-05-02T20:13:06Z | Exchangeable Variable Models | A sequence of random variables is exchangeable if its joint distribution is
invariant under variable permutations. We introduce exchangeable variable
models (EVMs) as a novel class of probabilistic models whose basic building
blocks are partially exchangeable sequences, a generalization of exchangeable
sequences. We prove that a family of tractable EVMs is optimal under zero-one
loss for a large class of functions, including parity and threshold functions,
and strictly subsumes existing tractable independence-based model families.
Extensive experiments show that EVMs outperform state of the art classifiers
such as SVMs and probabilistic models which are solely based on independence
assumptions.
| [
"['Mathias Niepert' 'Pedro Domingos']",
"Mathias Niepert and Pedro Domingos"
]
|
cs.LG cs.FL | null | 1405.0514 | null | null | http://arxiv.org/pdf/1405.0514v2 | 2014-11-27T18:59:45Z | 2014-05-02T20:58:39Z | Complexity of Equivalence and Learning for Multiplicity Tree Automata | We consider the complexity of equivalence and learning for multiplicity tree
automata, i.e., weighted tree automata over a field. We first show that the
equivalence problem is logspace equivalent to polynomial identity testing, the
complexity of which is a longstanding open problem. Secondly, we derive lower
bounds on the number of queries needed to learn multiplicity tree automata in
Angluin's exact learning model, over both arbitrary and fixed fields.
Habrard and Oncina (2006) give an exact learning algorithm for multiplicity
tree automata, in which the number of queries is proportional to the size of
the target automaton and the size of a largest counterexample, represented as a
tree, that is returned by the Teacher. However, the smallest
tree-counterexample may be exponential in the size of the target automaton.
Thus the above algorithm does not run in time polynomial in the size of the
target automaton, and has query complexity exponential in the lower bound.
Assuming a Teacher that returns minimal DAG representations of
counterexamples, we give a new exact learning algorithm whose query complexity
is quadratic in the target automaton size, almost matching the lower bound, and
improving the best previously-known algorithm by an exponential factor.
| [
"Ines Marusic and James Worrell",
"['Ines Marusic' 'James Worrell']"
]
|
cs.LG stat.ML | null | 1405.0586 | null | null | http://arxiv.org/pdf/1405.0586v3 | 2016-09-13T18:06:14Z | 2014-05-03T13:36:59Z | On Lipschitz Continuity and Smoothness of Loss Functions in Learning to
Rank | In binary classification and regression problems, it is well understood that
Lipschitz continuity and smoothness of the loss function play key roles in
governing generalization error bounds for empirical risk minimization
algorithms. In this paper, we show how these two properties affect
generalization error bounds in the learning to rank problem. The learning to
rank problem involves vector valued predictions and therefore the choice of the
norm with respect to which Lipschitz continuity and smoothness are defined
becomes crucial. Choosing the $\ell_\infty$ norm in our definition of Lipschitz
continuity allows us to improve existing bounds. Furthermore, under smoothness
assumptions, our choice enables us to prove rates that interpolate between
$1/\sqrt{n}$ and $1/n$ rates. Application of our results to ListNet, a popular
learning to rank method, gives state-of-the-art performance guarantees.
| [
"Ambuj Tewari and Sougata Chaudhuri",
"['Ambuj Tewari' 'Sougata Chaudhuri']"
]
|
cs.LG stat.ML | null | 1405.0591 | null | null | http://arxiv.org/pdf/1405.0591v1 | 2014-05-03T14:38:47Z | 2014-05-03T14:38:47Z | Perceptron-like Algorithms and Generalization Bounds for Learning to
Rank | Learning to rank is a supervised learning problem where the output space is
the space of rankings but the supervision space is the space of relevance
scores. We make theoretical contributions to the learning to rank problem both
in the online and batch settings. First, we propose a perceptron-like algorithm
for learning a ranking function in an online setting. Our algorithm is an
extension of the classic perceptron algorithm for the classification problem.
Second, in the setting of batch learning, we introduce a sufficient condition
for convex ranking surrogates to ensure a generalization bound that is
independent of number of objects per query. Our bound holds when linear ranking
functions are used: a common practice in many learning to rank algorithms. En
route to developing the online algorithm and generalization bound, we propose a
novel family of listwise large margin ranking surrogates. Our novel surrogate
family is obtained by modifying a well-known pairwise large margin ranking
surrogate and is distinct from the listwise large margin surrogates developed
using the structured prediction framework. Using the proposed family, we
provide a guaranteed upper bound on the cumulative NDCG (or MAP) induced loss
under the perceptron-like algorithm. We also show that the novel surrogates
satisfy the generalization bound condition.
| [
"Sougata Chaudhuri and Ambuj Tewari",
"['Sougata Chaudhuri' 'Ambuj Tewari']"
]
|
cs.IT cs.LG math.IT math.ST stat.TH | null | 1405.0782 | null | null | http://arxiv.org/pdf/1405.0782v2 | 2014-06-21T01:08:17Z | 2014-05-05T05:23:30Z | Optimality guarantees for distributed statistical estimation | Large data sets often require performing distributed statistical estimation,
with a full data set split across multiple machines and limited communication
between machines. To study such scenarios, we define and study some refinements
of the classical minimax risk that apply to distributed settings, comparing to
the performance of estimators with access to the entire data. Lower bounds on
these quantities provide a precise characterization of the minimum amount of
communication required to achieve the centralized minimax risk. We study two
classes of distributed protocols: one in which machines send messages
independently over channels without feedback, and a second allowing for
interactive communication, in which a central server broadcasts the messages
from a given machine to all other machines. We establish lower bounds for a
variety of problems, including location estimation in several families and
parameter estimation in different types of regression models. Our results
include a novel class of quantitative data-processing inequalities used to
characterize the effects of limited communication.
| [
"John C. Duchi and Michael I. Jordan and Martin J. Wainwright and\n Yuchen Zhang",
"['John C. Duchi' 'Michael I. Jordan' 'Martin J. Wainwright' 'Yuchen Zhang']"
]
|
cs.LG | null | 1405.0792 | null | null | http://arxiv.org/pdf/1405.0792v1 | 2014-05-05T06:49:05Z | 2014-05-05T06:49:05Z | On Exact Learning Monotone DNF from Membership Queries | In this paper, we study the problem of learning a monotone DNF with at most
$s$ terms of size (number of variables in each term) at most $r$ ($s$ term
$r$-MDNF) from membership queries. This problem is equivalent to the problem of
learning a general hypergraph using hyperedge-detecting queries, a problem
motivated by applications arising in chemical reactions and genome sequencing.
We first present new lower bounds for this problem and then present
deterministic and randomized adaptive algorithms with query complexities that
are almost optimal. All the algorithms we present in this paper run in time
linear in the query complexity and the number of variables $n$. In addition,
all of the algorithms we present in this paper are asymptotically tight for
fixed $r$ and/or $s$.
| [
"['Hasan Abasi' 'Nader H. Bshouty' 'Hanna Mazzawi']",
"Hasan Abasi and Nader H. Bshouty and Hanna Mazzawi"
]
|
cs.LG stat.ML | null | 1405.0833 | null | null | http://arxiv.org/pdf/1405.0833v1 | 2014-05-05T09:29:17Z | 2014-05-05T09:29:17Z | Generalized Risk-Aversion in Stochastic Multi-Armed Bandits | We consider the problem of minimizing the regret in stochastic multi-armed
bandit, when the measure of goodness of an arm is not the mean return, but some
general function of the mean and the variance.We characterize the conditions
under which learning is possible and present examples for which no natural
algorithm can achieve sublinear regret.
| [
"['Alexander Zimin' 'Rasmus Ibsen-Jensen' 'Krishnendu Chatterjee']",
"Alexander Zimin and Rasmus Ibsen-Jensen and Krishnendu Chatterjee"
]
|
cs.AI cs.LG stat.ML | null | 1405.0869 | null | null | http://arxiv.org/pdf/1405.0869v1 | 2014-05-05T12:01:24Z | 2014-05-05T12:01:24Z | Robust Subspace Outlier Detection in High Dimensional Space | Rare data in a large-scale database are called outliers that reveal
significant information in the real world. The subspace-based outlier detection
is regarded as a feasible approach in very high dimensional space. However, the
outliers found in subspaces are only part of the true outliers in high
dimensional space, indeed. The outliers hidden in normal-clustered points are
sometimes neglected in the projected dimensional subspace. In this paper, we
propose a robust subspace method for detecting such inner outliers in a given
dataset, which uses two dimensional-projections: detecting outliers in
subspaces with local density ratio in the first projected dimensions; finding
outliers by comparing neighbor's positions in the second projected dimensions.
Each point's weight is calculated by summing up all related values got in the
two steps projected dimensions, and then the points scoring the largest weight
values are taken as outliers. By taking a series of experiments with the number
of dimensions from 10 to 10000, the results show that our proposed method
achieves high precision in the case of extremely high dimensional space, and
works well in low dimensional space.
| [
"Zhana Bao",
"['Zhana Bao']"
]
|
cs.CV cs.LG | null | 1405.1005 | null | null | http://arxiv.org/pdf/1405.1005v2 | 2014-09-27T18:35:35Z | 2014-05-05T19:26:58Z | Comparing apples to apples in the evaluation of binary coding methods | We discuss methodological issues related to the evaluation of unsupervised
binary code construction methods for nearest neighbor search. These issues have
been widely ignored in literature. These coding methods attempt to preserve
either Euclidean distance or angular (cosine) distance in the binary embedding
space. We explain why when comparing a method whose goal is preserving cosine
similarity to one designed for preserving Euclidean distance, the original
features should be normalized by mapping them to the unit hypersphere before
learning the binary mapping functions. To compare a method whose goal is to
preserves Euclidean distance to one that preserves cosine similarity, the
original feature data must be mapped to a higher dimension by including a bias
term in binary mapping functions. These conditions ensure the fair comparison
between different binary code methods for the task of nearest neighbor search.
Our experiments show under these conditions the very simple methods (e.g. LSH
and ITQ) often outperform recent state-of-the-art methods (e.g. MDSH and
OK-means).
| [
"Mohammad Rastegari, Shobeir Fakhraei, Jonghyun Choi, David Jacobs,\n Larry S. Davis",
"['Mohammad Rastegari' 'Shobeir Fakhraei' 'Jonghyun Choi' 'David Jacobs'\n 'Larry S. Davis']"
]
|
cs.AI cs.LG stat.ML | null | 1405.1027 | null | null | http://arxiv.org/pdf/1405.1027v1 | 2014-05-05T12:06:06Z | 2014-05-05T12:06:06Z | K-NS: Section-Based Outlier Detection in High Dimensional Space | Finding rare information hidden in a huge amount of data from the Internet is
a necessary but complex issue. Many researchers have studied this issue and
have found effective methods to detect anomaly data in low dimensional space.
However, as the dimension increases, most of these existing methods perform
poorly in detecting outliers because of "high dimensional curse". Even though
some approaches aim to solve this problem in high dimensional space, they can
only detect some anomaly data appearing in low dimensional space and cannot
detect all of anomaly data which appear differently in high dimensional space.
To cope with this problem, we propose a new k-nearest section-based method
(k-NS) in a section-based space. Our proposed approach not only detects
outliers in low dimensional space with section-density ratio but also detects
outliers in high dimensional space with the ratio of k-nearest section against
average value. After taking a series of experiments with the dimension from 10
to 10000, the experiment results show that our proposed method achieves 100%
precision and 100% recall result in the case of extremely high dimensional
space, and better improvement in low dimensional space compared to our
previously proposed method.
| [
"Zhana Bao",
"['Zhana Bao']"
]
|
cs.LG cs.IT math.IT stat.ML | null | 1405.1119 | null | null | http://arxiv.org/pdf/1405.1119v2 | 2015-02-01T12:00:07Z | 2014-05-06T01:17:26Z | Feature selection for classification with class-separability strategy
and data envelopment analysis | In this paper, a novel feature selection method is presented, which is based
on Class-Separability (CS) strategy and Data Envelopment Analysis (DEA). To
better capture the relationship between features and the class, class labels
are separated into individual variables and relevance and redundancy are
explicitly handled on each class label. Super-efficiency DEA is employed to
evaluate and rank features via their conditional dependence scores on all class
labels, and the feature with maximum super-efficiency score is then added in
the conditioning set for conditional dependence estimation in the next
iteration, in such a way as to iteratively select features and get the final
selected features. Eventually, experiments are conducted to evaluate the
effectiveness of proposed method comparing with four state-of-the-art methods
from the viewpoint of classification accuracy. Empirical results verify the
feasibility and the superiority of proposed feature selection method.
| [
"['Yishi Zhang' 'Chao Yang' 'Anrong Yang' 'Chan Xiong' 'Xingchi Zhou'\n 'Zigang Zhang']",
"Yishi Zhang, Chao Yang, Anrong Yang, Chan Xiong, Xingchi Zhou, Zigang\n Zhang"
]
|
stat.ML cs.LG | 10.1016/j.neucom.2014.05.094 | 1405.1297 | null | null | http://arxiv.org/abs/1405.1297v2 | 2016-06-03T16:10:19Z | 2014-05-06T15:05:02Z | Combining Multiple Clusterings via Crowd Agreement Estimation and
Multi-Granularity Link Analysis | The clustering ensemble technique aims to combine multiple clusterings into a
probably better and more robust clustering and has been receiving an increasing
attention in recent years. There are mainly two aspects of limitations in the
existing clustering ensemble approaches. Firstly, many approaches lack the
ability to weight the base clusterings without access to the original data and
can be affected significantly by the low-quality, or even ill clusterings.
Secondly, they generally focus on the instance level or cluster level in the
ensemble system and fail to integrate multi-granularity cues into a unified
model. To address these two limitations, this paper proposes to solve the
clustering ensemble problem via crowd agreement estimation and
multi-granularity link analysis. We present the normalized crowd agreement
index (NCAI) to evaluate the quality of base clusterings in an unsupervised
manner and thus weight the base clusterings in accordance with their clustering
validity. To explore the relationship between clusters, the source aware
connected triple (SACT) similarity is introduced with regard to their common
neighbors and the source reliability. Based on NCAI and multi-granularity
information collected among base clusterings, clusters, and data instances, we
further propose two novel consensus functions, termed weighted evidence
accumulation clustering (WEAC) and graph partitioning with multi-granularity
link analysis (GP-MGLA) respectively. The experiments are conducted on eight
real-world datasets. The experimental results demonstrate the effectiveness and
robustness of the proposed methods.
| [
"Dong Huang and Jian-Huang Lai and Chang-Dong Wang",
"['Dong Huang' 'Jian-Huang Lai' 'Chang-Dong Wang']"
]
|
cs.CE cs.LG | 10.14445/22312803/IJCTT-V10P137 | 1405.1304 | null | null | http://arxiv.org/abs/1405.1304v1 | 2014-05-03T14:26:42Z | 2014-05-03T14:26:42Z | Application of Machine Learning Techniques in Aquaculture | In this paper we present applications of different machine learning
algorithms in aquaculture. Machine learning algorithms learn models from
historical data. In aquaculture historical data are obtained from farm
practices, yields, and environmental data sources. Associations between these
different variables can be obtained by applying machine learning algorithms to
historical data. In this paper we present applications of different machine
learning algorithms in aquaculture applications.
| [
"Akhlaqur Rahman and Sumaira Tasnim",
"['Akhlaqur Rahman' 'Sumaira Tasnim']"
]
|
stat.ML cs.LG cs.NE | null | 1405.1380 | null | null | http://arxiv.org/pdf/1405.1380v4 | 2015-06-15T23:52:59Z | 2014-05-06T17:41:33Z | Is Joint Training Better for Deep Auto-Encoders? | Traditionally, when generative models of data are developed via deep
architectures, greedy layer-wise pre-training is employed. In a well-trained
model, the lower layer of the architecture models the data distribution
conditional upon the hidden variables, while the higher layers model the hidden
distribution prior. But due to the greedy scheme of the layerwise training
technique, the parameters of lower layers are fixed when training higher
layers. This makes it extremely challenging for the model to learn the hidden
distribution prior, which in turn leads to a suboptimal model for the data
distribution. We therefore investigate joint training of deep autoencoders,
where the architecture is viewed as one stack of two or more single-layer
autoencoders. A single global reconstruction objective is jointly optimized,
such that the objective for the single autoencoders at each layer acts as a
local, layer-level regularizer. We empirically evaluate the performance of this
joint training scheme and observe that it not only learns a better data model,
but also learns better higher layer representations, which highlights its
potential for unsupervised feature learning. In addition, we find that the
usage of regularizations in the joint training scheme is crucial in achieving
good performance. In the supervised setting, joint training also shows superior
performance when training deeper models. The joint training framework can thus
provide a platform for investigating more efficient usage of different types of
regularizers, especially in light of the growing volumes of available unlabeled
data.
| [
"['Yingbo Zhou' 'Devansh Arpit' 'Ifeoma Nwogu' 'Venu Govindaraju']",
"Yingbo Zhou, Devansh Arpit, Ifeoma Nwogu, Venu Govindaraju"
]
|
cs.NE cs.LG stat.ML | null | 1405.1436 | null | null | http://arxiv.org/pdf/1405.1436v1 | 2014-05-06T20:02:46Z | 2014-05-06T20:02:46Z | Training Restricted Boltzmann Machine by Perturbation | A new approach to maximum likelihood learning of discrete graphical models
and RBM in particular is introduced. Our method, Perturb and Descend (PD) is
inspired by two ideas (I) perturb and MAP method for sampling (II) learning by
Contrastive Divergence minimization. In contrast to perturb and MAP, PD
leverages training data to learn the models that do not allow efficient MAP
estimation. During the learning, to produce a sample from the current model, we
start from a training data and descend in the energy landscape of the
"perturbed model", for a fixed number of steps, or until a local optima is
reached. For RBM, this involves linear calculations and thresholding which can
be very fast. Furthermore we show that the amount of perturbation is closely
related to the temperature parameter and it can regularize the model by
producing robust features resulting in sparse hidden layer activation.
| [
"Siamak Ravanbakhsh, Russell Greiner, Brendan Frey",
"['Siamak Ravanbakhsh' 'Russell Greiner' 'Brendan Frey']"
]
|
cs.LG | null | 1405.1503 | null | null | http://arxiv.org/pdf/1405.1503v3 | 2015-02-21T02:23:24Z | 2014-05-07T04:39:01Z | Adaptation Algorithm and Theory Based on Generalized Discrepancy | We present a new algorithm for domain adaptation improving upon a discrepancy
minimization algorithm previously shown to outperform a number of algorithms
for this task. Unlike many previous algorithms for domain adaptation, our
algorithm does not consist of a fixed reweighting of the losses over the
training sample. We show that our algorithm benefits from a solid theoretical
foundation and more favorable learning bounds than discrepancy minimization. We
present a detailed description of our algorithm and give several efficient
solutions for solving its optimization problem. We also report the results of
several experiments showing that it outperforms discrepancy minimization.
| [
"['Corinna Cortes' 'Mehryar Mohri' 'Andres Muñoz Medina']",
"Corinna Cortes and Mehryar Mohri and Andres Mu\\~noz Medina"
]
|
cs.LG cs.AI cs.IT math.IT | null | 1405.1513 | null | null | http://arxiv.org/pdf/1405.1513v1 | 2014-05-07T06:10:47Z | 2014-05-07T06:10:47Z | A Mathematical Theory of Learning | In this paper, a mathematical theory of learning is proposed that has many
parallels with information theory. We consider Vapnik's General Setting of
Learning in which the learning process is defined to be the act of selecting a
hypothesis in response to a given training set. Such hypothesis can, for
example, be a decision boundary in classification, a set of centroids in
clustering, or a set of frequent item-sets in association rule mining.
Depending on the hypothesis space and how the final hypothesis is selected, we
show that a learning process can be assigned a numeric score, called learning
capacity, which is analogous to Shannon's channel capacity and satisfies
similar interesting properties as well such as the data-processing inequality
and the information-cannot-hurt inequality. In addition, learning capacity
provides the tightest possible bound on the difference between true risk and
empirical risk of the learning process for all loss functions that are
parametrized by the chosen hypothesis. It is also shown that the notion of
learning capacity equivalently quantifies how sensitive the choice of the final
hypothesis is to a small perturbation in the training set. Consequently,
algorithmic stability is both necessary and sufficient for generalization.
While the theory does not rely on concentration inequalities, we finally show
that analogs to classical results in learning theory using the Probably
Approximately Correct (PAC) model can be immediately deduced using this theory,
and conclude with information-theoretic bounds to learning capacity.
| [
"['Ibrahim Alabdulmohsin']",
"Ibrahim Alabdulmohsin"
]
|
math.ST cs.LG stat.ML stat.TH | null | 1405.1533 | null | null | http://arxiv.org/pdf/1405.1533v2 | 2014-05-08T20:12:02Z | 2014-05-07T08:33:41Z | A consistent deterministic regression tree for non-parametric prediction
of time series | We study online prediction of bounded stationary ergodic processes. To do so,
we consider the setting of prediction of individual sequences and build a
deterministic regression tree that performs asymptotically as well as the best
L-Lipschitz constant predictors. Then, we show why the obtained regret bound
entails the asymptotical optimality with respect to the class of bounded
stationary ergodic processes.
| [
"Pierre Gaillard (GREGH), Paul Baudin (INRIA Rocquencourt)",
"['Pierre Gaillard' 'Paul Baudin']"
]
|
null | null | 1405.1535 | null | null | http://arxiv.org/pdf/1405.1535v1 | 2014-05-07T09:06:28Z | 2014-05-07T09:06:28Z | Learning Boolean Halfspaces with Small Weights from Membership Queries | We consider the problem of proper learning a Boolean Halfspace with integer weights ${0,1,ldots,t}$ from membership queries only. The best known algorithm for this problem is an adaptive algorithm that asks $n^{O(t^5)}$ membership queries where the best lower bound for the number of membership queries is $n^t$ [Learning Threshold Functions with Small Weights Using Membership Queries. COLT 1999] In this paper we close this gap and give an adaptive proper learning algorithm with two rounds that asks $n^{O(t)}$ membership queries. We also give a non-adaptive proper learning algorithm that asks $n^{O(t^3)}$ membership queries. | [
"['Hasan Abasi' 'Ali Z. Abdi' 'Nader H. Bshouty']"
]
|
cs.LG cs.IT math.IT | null | 1405.1665 | null | null | http://arxiv.org/pdf/1405.1665v2 | 2014-11-08T03:06:04Z | 2014-05-07T16:44:21Z | On Communication Cost of Distributed Statistical Estimation and
Dimensionality | We explore the connection between dimensionality and communication cost in
distributed learning problems. Specifically we study the problem of estimating
the mean $\vec{\theta}$ of an unknown $d$ dimensional gaussian distribution in
the distributed setting. In this problem, the samples from the unknown
distribution are distributed among $m$ different machines. The goal is to
estimate the mean $\vec{\theta}$ at the optimal minimax rate while
communicating as few bits as possible. We show that in this setting, the
communication cost scales linearly in the number of dimensions i.e. one needs
to deal with different dimensions individually. Applying this result to
previous lower bounds for one dimension in the interactive setting
\cite{ZDJW13} and to our improved bounds for the simultaneous setting, we prove
new lower bounds of $\Omega(md/\log(m))$ and $\Omega(md)$ for the bits of
communication needed to achieve the minimax squared loss, in the interactive
and simultaneous settings respectively. To complement, we also demonstrate an
interactive protocol achieving the minimax squared loss with $O(md)$ bits of
communication, which improves upon the simple simultaneous protocol by a
logarithmic factor. Given the strong lower bounds in the general setting, we
initiate the study of the distributed parameter estimation problems with
structured parameters. Specifically, when the parameter is promised to be
$s$-sparse, we show a simple thresholding based protocol that achieves the same
squared loss while saving a $d/s$ factor of communication. We conjecture that
the tradeoff between communication and squared loss demonstrated by this
protocol is essentially optimal up to logarithmic factor.
| [
"Ankit Garg and Tengyu Ma and Huy L. Nguyen",
"['Ankit Garg' 'Tengyu Ma' 'Huy L. Nguyen']"
]
|
cs.CV cs.LG | null | 1405.1966 | null | null | http://arxiv.org/pdf/1405.1966v1 | 2014-04-15T16:52:38Z | 2014-04-15T16:52:38Z | Texture Based Image Segmentation of Chili Pepper X-Ray Images Using
Gabor Filter | Texture segmentation is the process of partitioning an image into regions
with different textures containing a similar group of pixels. Detecting the
discontinuity of the filter's output and their statistical properties help in
segmenting and classifying a given image with different texture regions. In
this proposed paper, chili x-ray image texture segmentation is performed by
using Gabor filter. The texture segmented result obtained from Gabor filter fed
into three texture filters, namely Entropy, Standard Deviation and Range
filter. After performing texture analysis, features can be extracted by using
Statistical methods. In this paper Gray Level Co-occurrence Matrices and First
order statistics are used as feature extraction methods. Features extracted
from statistical methods are given to Support Vector Machine (SVM) classifier.
Using this methodology, it is found that texture segmentation is followed by
the Gray Level Co-occurrence Matrix feature extraction method gives a higher
accuracy rate of 84% when compared with First order feature extraction method.
Key Words: Texture segmentation, Texture filter, Gabor filter, Feature
extraction methods, SVM classifier.
| [
"M.Rajalakshmi and Dr. P.Subashini",
"['M. Rajalakshmi' 'Dr. P. Subashini']"
]
|
cs.LG cs.CV | null | 1405.2102 | null | null | http://arxiv.org/pdf/1405.2102v1 | 2014-05-08T21:29:04Z | 2014-05-08T21:29:04Z | Improving Image Clustering using Sparse Text and the Wisdom of the
Crowds | We propose a method to improve image clustering using sparse text and the
wisdom of the crowds. In particular, we present a method to fuse two different
kinds of document features, image and text features, and use a common
dictionary or "wisdom of the crowds" as the connection between the two
different kinds of documents. With the proposed fusion matrix, we use topic
modeling via non-negative matrix factorization to cluster documents.
| [
"['Anna Ma' 'Arjuna Flenner' 'Deanna Needell' 'Allon G. Percus']",
"Anna Ma, Arjuna Flenner, Deanna Needell, Allon G. Percus"
]
|
cs.NE cs.LG | null | 1405.2262 | null | null | http://arxiv.org/pdf/1405.2262v1 | 2014-05-09T15:23:06Z | 2014-05-09T15:23:06Z | Training Deep Fourier Neural Networks To Fit Time-Series Data | We present a method for training a deep neural network containing sinusoidal
activation functions to fit to time-series data. Weights are initialized using
a fast Fourier transform, then trained with regularization to improve
generalization. A simple dynamic parameter tuning method is employed to adjust
both the learning rate and regularization term, such that stability and
efficient training are both achieved. We show how deeper layers can be utilized
to model the observed sequence using a sparser set of sinusoid units, and how
non-uniform regularization can improve generalization by promoting the shifting
of weight toward simpler units. The method is demonstrated with time-series
problems to show that it leads to effective extrapolation of nonlinear trends.
| [
"['Michael S. Gashler' 'Stephen C. Ashmore']",
"Michael S. Gashler and Stephen C. Ashmore"
]
|
cs.LG astro-ph.IM stat.ML | null | 1405.2278 | null | null | http://arxiv.org/pdf/1405.2278v1 | 2014-05-09T16:14:47Z | 2014-05-09T16:14:47Z | Hellinger Distance Trees for Imbalanced Streams | Classifiers trained on data sets possessing an imbalanced class distribution
are known to exhibit poor generalisation performance. This is known as the
imbalanced learning problem. The problem becomes particularly acute when we
consider incremental classifiers operating on imbalanced data streams,
especially when the learning objective is rare class identification. As
accuracy may provide a misleading impression of performance on imbalanced data,
existing stream classifiers based on accuracy can suffer poor minority class
performance on imbalanced streams, with the result being low minority class
recall rates. In this paper we address this deficiency by proposing the use of
the Hellinger distance measure, as a very fast decision tree split criterion.
We demonstrate that by using Hellinger a statistically significant improvement
in recall rates on imbalanced data streams can be achieved, with an acceptable
increase in the false positive rate.
| [
"R. J. Lyon, J. M. Brooke, J. D. Knowles, B. W. Stappers",
"['R. J. Lyon' 'J. M. Brooke' 'J. D. Knowles' 'B. W. Stappers']"
]
|
cs.LG stat.ML | null | 1405.2294 | null | null | http://arxiv.org/pdf/1405.2294v2 | 2016-12-14T02:06:49Z | 2014-04-25T15:52:47Z | Nonparametric Detection of Anomalous Data Streams | A nonparametric anomalous hypothesis testing problem is investigated, in
which there are totally n sequences with s anomalous sequences to be detected.
Each typical sequence contains m independent and identically distributed
(i.i.d.) samples drawn from a distribution p, whereas each anomalous sequence
contains m i.i.d. samples drawn from a distribution q that is distinct from p.
The distributions p and q are assumed to be unknown in advance.
Distribution-free tests are constructed using maximum mean discrepancy as the
metric, which is based on mean embeddings of distributions into a reproducing
kernel Hilbert space. The probability of error is bounded as a function of the
sample size m, the number s of anomalous sequences and the number n of
sequences. It is then shown that with s known, the constructed test is
exponentially consistent if m is greater than a constant factor of log n, for
any p and q, whereas with s unknown, m should has an order strictly greater
than log n. Furthermore, it is shown that no test can be consistent for
arbitrary p and q if m is less than a constant factor of log n, thus the
order-level optimality of the proposed test is established. Numerical results
are provided to demonstrate that our tests outperform (or perform as well as)
the tests based on other competitive approaches under various cases.
| [
"Shaofeng Zou, Yingbin Liang, H. Vincent Poor, Xinghua Shi",
"['Shaofeng Zou' 'Yingbin Liang' 'H. Vincent Poor' 'Xinghua Shi']"
]
|
stat.ML cs.LG stat.ME | null | 1405.2377 | null | null | http://arxiv.org/pdf/1405.2377v1 | 2014-05-10T02:03:22Z | 2014-05-10T02:03:22Z | A Hybrid Monte Carlo Architecture for Parameter Optimization | Much recent research has been conducted in the area of Bayesian learning,
particularly with regard to the optimization of hyper-parameters via Gaussian
process regression. The methodologies rely chiefly on the method of maximizing
the expected improvement of a score function with respect to adjustments in the
hyper-parameters. In this work, we present a novel algorithm that exploits
notions of confidence intervals and uncertainties to enable the discovery of
the best optimal within a targeted region of the parameter space. We
demonstrate the efficacy of our algorithm with respect to machine learning
problems and show cases where our algorithm is competitive with the method of
maximizing expected improvement.
| [
"James Brofos",
"['James Brofos']"
]
|
cs.LG | null | 1405.2420 | null | null | http://arxiv.org/pdf/1405.2420v1 | 2014-05-10T11:23:08Z | 2014-05-10T11:23:08Z | Optimal Learners for Multiclass Problems | The fundamental theorem of statistical learning states that for binary
classification problems, any Empirical Risk Minimization (ERM) learning rule
has close to optimal sample complexity. In this paper we seek for a generic
optimal learner for multiclass prediction. We start by proving a surprising
result: a generic optimal multiclass learner must be improper, namely, it must
have the ability to output hypotheses which do not belong to the hypothesis
class, even though it knows that all the labels are generated by some
hypothesis from the class. In particular, no ERM learner is optimal. This
brings back the fundmamental question of "how to learn"? We give a complete
answer to this question by giving a new analysis of the one-inclusion
multiclass learner of Rubinstein et al (2006) showing that its sample
complexity is essentially optimal. Then, we turn to study the popular
hypothesis class of generalized linear classifiers. We derive optimal learners
that, unlike the one-inclusion algorithm, are computationally efficient.
Furthermore, we show that the sample complexity of these learners is better
than the sample complexity of the ERM rule, thus settling in negative an open
question due to Collins (2005).
| [
"Amit Daniely and Shai Shalev-Shwartz",
"['Amit Daniely' 'Shai Shalev-Shwartz']"
]
|
stat.ML cs.LG | null | 1405.2432 | null | null | http://arxiv.org/pdf/1405.2432v1 | 2014-05-10T13:34:22Z | 2014-05-10T13:34:22Z | Functional Bandits | We introduce the functional bandit problem, where the objective is to find an
arm that optimises a known functional of the unknown arm-reward distributions.
These problems arise in many settings such as maximum entropy methods in
natural language processing, and risk-averse decision-making, but current
best-arm identification techniques fail in these domains. We propose a new
approach, that combines functional estimation and arm elimination, to tackle
this problem. This method achieves provably efficient performance guarantees.
In addition, we illustrate this method on a number of important functionals in
risk management and information theory, and refine our generic theoretical
results in those cases.
| [
"['Long Tran-Thanh' 'Jia Yuan Yu']",
"Long Tran-Thanh and Jia Yuan Yu"
]
|
cs.LG | null | 1405.2476 | null | null | http://arxiv.org/pdf/1405.2476v4 | 2016-10-10T20:56:01Z | 2014-05-10T22:30:38Z | A Canonical Semi-Deterministic Transducer | We prove the existence of a canonical form for semi-deterministic transducers
with incomparable sets of output strings. Based on this, we develop an
algorithm which learns semi-deterministic transducers given access to
translation queries. We also prove that there is no learning algorithm for
semi-deterministic transducers that uses only domain knowledge.
| [
"Achilles Beros, Colin de la Higuera",
"['Achilles Beros' 'Colin de la Higuera']"
]
|
cs.AI cs.LG stat.ML | null | 1405.2600 | null | null | http://arxiv.org/pdf/1405.2600v4 | 2017-06-03T12:03:19Z | 2014-05-11T23:11:52Z | Learning from networked examples | Many machine learning algorithms are based on the assumption that training
examples are drawn independently. However, this assumption does not hold
anymore when learning from a networked sample because two or more training
examples may share some common objects, and hence share the features of these
shared objects. We show that the classic approach of ignoring this problem
potentially can have a harmful effect on the accuracy of statistics, and then
consider alternatives. One of these is to only use independent examples,
discarding other information. However, this is clearly suboptimal. We analyze
sample error bounds in this networked setting, providing significantly improved
results. An important component of our approach is formed by efficient sample
weighting schemes, which leads to novel concentration inequalities.
| [
"Yuyi Wang and Jan Ramon and Zheng-Chu Guo",
"['Yuyi Wang' 'Jan Ramon' 'Zheng-Chu Guo']"
]
|
stat.ML cs.LG | null | 1405.2606 | null | null | http://arxiv.org/pdf/1405.2606v1 | 2014-05-12T00:26:12Z | 2014-05-12T00:26:12Z | Structural Return Maximization for Reinforcement Learning | Batch Reinforcement Learning (RL) algorithms attempt to choose a policy from
a designer-provided class of policies given a fixed set of training data.
Choosing the policy which maximizes an estimate of return often leads to
over-fitting when only limited data is available, due to the size of the policy
class in relation to the amount of data available. In this work, we focus on
learning policy classes that are appropriately sized to the amount of data
available. We accomplish this by using the principle of Structural Risk
Minimization, from Statistical Learning Theory, which uses Rademacher
complexity to identify a policy class that maximizes a bound on the return of
the best policy in the chosen policy class, given the available data. Unlike
similar batch RL approaches, our bound on return requires only extremely weak
assumptions on the true system.
| [
"['Joshua Joseph' 'Javier Velez' 'Nicholas Roy']",
"Joshua Joseph, Javier Velez, Nicholas Roy"
]
|
math.PR cs.LG stat.ML | null | 1405.2639 | null | null | http://arxiv.org/pdf/1405.2639v4 | 2015-12-01T21:15:23Z | 2014-05-12T06:32:49Z | Sharp Finite-Time Iterated-Logarithm Martingale Concentration | We give concentration bounds for martingales that are uniform over finite
times and extend classical Hoeffding and Bernstein inequalities. We also
demonstrate our concentration bounds to be optimal with a matching
anti-concentration inequality, proved using the same method. Together these
constitute a finite-time version of the law of the iterated logarithm, and shed
light on the relationship between it and the central limit theorem.
| [
"Akshay Balsubramani",
"['Akshay Balsubramani']"
]
|
cs.LG | null | 1405.2652 | null | null | http://arxiv.org/pdf/1405.2652v6 | 2014-09-15T08:32:45Z | 2014-05-12T07:45:54Z | Selecting Near-Optimal Approximate State Representations in
Reinforcement Learning | We consider a reinforcement learning setting introduced in (Maillard et al.,
NIPS 2011) where the learner does not have explicit access to the states of the
underlying Markov decision process (MDP). Instead, she has access to several
models that map histories of past interactions to states. Here we improve over
known regret bounds in this setting, and more importantly generalize to the
case where the models given to the learner do not contain a true model
resulting in an MDP representation but only approximations of it. We also give
improved error bounds for state aggregation.
| [
"Ronald Ortner, Odalric-Ambrym Maillard, Daniil Ryabko",
"['Ronald Ortner' 'Odalric-Ambrym Maillard' 'Daniil Ryabko']"
]
|
cs.AI cs.LG stat.ML | 10.1162/NECO_a_00732 | 1405.2664 | null | null | http://arxiv.org/abs/1405.2664v2 | 2015-06-18T12:06:14Z | 2014-05-12T08:20:21Z | FastMMD: Ensemble of Circular Discrepancy for Efficient Two-Sample Test | The maximum mean discrepancy (MMD) is a recently proposed test statistic for
two-sample test. Its quadratic time complexity, however, greatly hampers its
availability to large-scale applications. To accelerate the MMD calculation, in
this study we propose an efficient method called FastMMD. The core idea of
FastMMD is to equivalently transform the MMD with shift-invariant kernels into
the amplitude expectation of a linear combination of sinusoid components based
on Bochner's theorem and Fourier transform (Rahimi & Recht, 2007). Taking
advantage of sampling of Fourier transform, FastMMD decreases the time
complexity for MMD calculation from $O(N^2 d)$ to $O(L N d)$, where $N$ and $d$
are the size and dimension of the sample set, respectively. Here $L$ is the
number of basis functions for approximating kernels which determines the
approximation accuracy. For kernels that are spherically invariant, the
computation can be further accelerated to $O(L N \log d)$ by using the Fastfood
technique (Le et al., 2013). The uniform convergence of our method has also
been theoretically proved in both unbiased and biased estimates. We have
further provided a geometric explanation for our method, namely ensemble of
circular discrepancy, which facilitates us to understand the insight of MMD,
and is hopeful to help arouse more extensive metrics for assessing two-sample
test. Experimental results substantiate that FastMMD is with similar accuracy
as exact MMD, while with faster computation speed and lower variance than the
existing MMD approximation methods.
| [
"['Ji Zhao' 'Deyu Meng']",
"Ji Zhao, Deyu Meng"
]
|
stat.ML cs.LG math.OC | null | 1405.2690 | null | null | http://arxiv.org/pdf/1405.2690v1 | 2014-05-12T09:59:59Z | 2014-05-12T09:59:59Z | Policy Gradients for CVaR-Constrained MDPs | We study a risk-constrained version of the stochastic shortest path (SSP)
problem, where the risk measure considered is Conditional Value-at-Risk (CVaR).
We propose two algorithms that obtain a locally risk-optimal policy by
employing four tools: stochastic approximation, mini batches, policy gradients
and importance sampling. Both the algorithms incorporate a CVaR estimation
procedure, along the lines of Bardou et al. [2009], which in turn is based on
Rockafellar-Uryasev's representation for CVaR and utilize the likelihood ratio
principle for estimating the gradient of the sum of one cost function
(objective of the SSP) and the gradient of the CVaR of the sum of another cost
function (in the constraint of SSP). The algorithms differ in the manner in
which they approximate the CVaR estimates/necessary gradients - the first
algorithm uses stochastic approximation, while the second employ mini-batches
in the spirit of Monte Carlo methods. We establish asymptotic convergence of
both the algorithms. Further, since estimating CVaR is related to rare-event
simulation, we incorporate an importance sampling based variance reduction
scheme into our proposed algorithms.
| [
"['Prashanth L. A.']",
"Prashanth L.A."
]
|
cs.LG cs.AI stat.ML | null | 1405.2798 | null | null | http://arxiv.org/pdf/1405.2798v1 | 2014-05-12T15:18:15Z | 2014-05-12T15:18:15Z | Two-Stage Metric Learning | In this paper, we present a novel two-stage metric learning algorithm. We
first map each learning instance to a probability distribution by computing its
similarities to a set of fixed anchor points. Then, we define the distance in
the input data space as the Fisher information distance on the associated
statistical manifold. This induces in the input data space a new family of
distance metric with unique properties. Unlike kernelized metric learning, we
do not require the similarity measure to be positive semi-definite. Moreover,
it can also be interpreted as a local metric learning algorithm with well
defined distance approximation. We evaluate its performance on a number of
datasets. It outperforms significantly other metric learning methods and SVM.
| [
"Jun Wang, Ke Sun, Fei Sha, Stephane Marchand-Maillet, Alexandros\n Kalousis",
"['Jun Wang' 'Ke Sun' 'Fei Sha' 'Stephane Marchand-Maillet'\n 'Alexandros Kalousis']"
]
|
cs.DS cs.GT cs.LG | null | 1405.2875 | null | null | http://arxiv.org/pdf/1405.2875v2 | 2015-09-02T04:21:07Z | 2014-05-12T18:52:28Z | Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms
for Repeated Principal-Agent Problems | Crowdsourcing markets have emerged as a popular platform for matching
available workers with tasks to complete. The payment for a particular task is
typically set by the task's requester, and may be adjusted based on the quality
of the completed work, for example, through the use of "bonus" payments. In
this paper, we study the requester's problem of dynamically adjusting
quality-contingent payments for tasks. We consider a multi-round version of the
well-known principal-agent model, whereby in each round a worker makes a
strategic choice of the effort level which is not directly observable by the
requester. In particular, our formulation significantly generalizes the
budget-free online task pricing problems studied in prior work.
We treat this problem as a multi-armed bandit problem, with each "arm"
representing a potential contract. To cope with the large (and in fact,
infinite) number of arms, we propose a new algorithm, AgnosticZooming, which
discretizes the contract space into a finite number of regions, effectively
treating each region as a single arm. This discretization is adaptively
refined, so that more promising regions of the contract space are eventually
discretized more finely. We analyze this algorithm, showing that it achieves
regret sublinear in the time horizon and substantially improves over
non-adaptive discretization (which is the only competing approach in the
literature).
Our results advance the state of art on several different topics: the theory
of crowdsourcing markets, principal-agent problems, multi-armed bandits, and
dynamic pricing.
| [
"Chien-Ju Ho, Aleksandrs Slivkins, Jennifer Wortman Vaughan",
"['Chien-Ju Ho' 'Aleksandrs Slivkins' 'Jennifer Wortman Vaughan']"
]
|
cs.AI cs.LG stat.ML | null | 1405.2878 | null | null | http://arxiv.org/pdf/1405.2878v1 | 2014-05-12T19:11:03Z | 2014-05-12T19:11:03Z | Approximate Policy Iteration Schemes: A Comparison | We consider the infinite-horizon discounted optimal control problem
formalized by Markov Decision Processes. We focus on several approximate
variations of the Policy Iteration algorithm: Approximate Policy Iteration,
Conservative Policy Iteration (CPI), a natural adaptation of the Policy Search
by Dynamic Programming algorithm to the infinite-horizon case (PSDP$_\infty$),
and the recently proposed Non-Stationary Policy iteration (NSPI(m)). For all
algorithms, we describe performance bounds, and make a comparison by paying a
particular attention to the concentrability constants involved, the number of
iterations and the memory required. Our analysis highlights the following
points: 1) The performance guarantee of CPI can be arbitrarily better than that
of API/API($\alpha$), but this comes at the cost of a relative---exponential in
$\frac{1}{\epsilon}$---increase of the number of iterations. 2) PSDP$_\infty$
enjoys the best of both worlds: its performance guarantee is similar to that of
CPI, but within a number of iterations similar to that of API. 3) Contrary to
API that requires a constant memory, the memory needed by CPI and PSDP$_\infty$
is proportional to their number of iterations, which may be problematic when
the discount factor $\gamma$ is close to 1 or the approximation error
$\epsilon$ is close to $0$; we show that the NSPI(m) algorithm allows to make
an overall trade-off between memory and performance. Simulations with these
schemes confirm our analysis.
| [
"['Bruno Scherrer']",
"Bruno Scherrer (INRIA Nancy - Grand Est / LORIA)"
]
|
stat.ML cs.LG math.OC | null | 1405.3080 | null | null | http://arxiv.org/pdf/1405.3080v1 | 2014-05-13T09:45:49Z | 2014-05-13T09:45:49Z | Accelerating Minibatch Stochastic Gradient Descent using Stratified
Sampling | Stochastic Gradient Descent (SGD) is a popular optimization method which has
been applied to many important machine learning tasks such as Support Vector
Machines and Deep Neural Networks. In order to parallelize SGD, minibatch
training is often employed. The standard approach is to uniformly sample a
minibatch at each step, which often leads to high variance. In this paper we
propose a stratified sampling strategy, which divides the whole dataset into
clusters with low within-cluster variance; we then take examples from these
clusters using a stratified sampling technique. It is shown that the
convergence rate can be significantly improved by the algorithm. Encouraging
experimental results confirm the effectiveness of the proposed method.
| [
"['Peilin Zhao' 'Tong Zhang']",
"Peilin Zhao, Tong Zhang"
]
|
stat.ML cs.LG | null | 1405.3162 | null | null | http://arxiv.org/pdf/1405.3162v1 | 2014-05-13T14:17:11Z | 2014-05-13T14:17:11Z | Circulant Binary Embedding | Binary embedding of high-dimensional data requires long codes to preserve the
discriminative power of the input space. Traditional binary coding methods
often suffer from very high computation and storage costs in such a scenario.
To address this problem, we propose Circulant Binary Embedding (CBE) which
generates binary codes by projecting the data with a circulant matrix. The
circulant structure enables the use of Fast Fourier Transformation to speed up
the computation. Compared to methods that use unstructured matrices, the
proposed method improves the time complexity from $\mathcal{O}(d^2)$ to
$\mathcal{O}(d\log{d})$, and the space complexity from $\mathcal{O}(d^2)$ to
$\mathcal{O}(d)$ where $d$ is the input dimensionality. We also propose a novel
time-frequency alternating optimization to learn data-dependent circulant
projections, which alternatively minimizes the objective in original and
Fourier domains. We show by extensive experiments that the proposed approach
gives much better performance than the state-of-the-art approaches for fixed
time, and provides much faster computation with no performance degradation for
fixed number of bits.
| [
"['Felix X. Yu' 'Sanjiv Kumar' 'Yunchao Gong' 'Shih-Fu Chang']",
"Felix X. Yu, Sanjiv Kumar, Yunchao Gong, Shih-Fu Chang"
]
|
cs.LG | null | 1405.3167 | null | null | http://arxiv.org/pdf/1405.3167v1 | 2014-05-13T14:36:59Z | 2014-05-13T14:36:59Z | Clustering, Hamming Embedding, Generalized LSH and the Max Norm | We study the convex relaxation of clustering and hamming embedding, focusing
on the asymmetric case (co-clustering and asymmetric hamming embedding),
understanding their relationship to LSH as studied by (Charikar 2002) and to
the max-norm ball, and the differences between their symmetric and asymmetric
versions.
| [
"['Behnam Neyshabur' 'Yury Makarychev' 'Nathan Srebro']",
"Behnam Neyshabur, Yury Makarychev, Nathan Srebro"
]
|
cs.LG cs.SI physics.soc-ph | null | 1405.3210 | null | null | http://arxiv.org/pdf/1405.3210v1 | 2014-05-13T16:08:55Z | 2014-05-13T16:08:55Z | Locally Boosted Graph Aggregation for Community Detection | Learning the right graph representation from noisy, multi-source data has
garnered significant interest in recent years. A central tenet of this problem
is relational learning. Here the objective is to incorporate the partial
information each data source gives us in a way that captures the true
underlying relationships. To address this challenge, we present a general,
boosting-inspired framework for combining weak evidence of entity associations
into a robust similarity metric. Building on previous work, we explore the
extent to which different local quality measurements yield graph
representations that are suitable for community detection. We present empirical
results on a variety of datasets demonstrating the utility of this framework,
especially with respect to real datasets where noise and scale present serious
challenges. Finally, we prove a convergence theorem in an ideal setting and
outline future research into other application domains.
| [
"Jeremy Kun, Rajmonda Caceres, Kevin Carter",
"['Jeremy Kun' 'Rajmonda Caceres' 'Kevin Carter']"
]
|
stat.CO cs.LG stat.ML | null | 1405.3222 | null | null | http://arxiv.org/pdf/1405.3222v2 | 2014-11-03T16:44:50Z | 2014-05-13T16:42:45Z | Efficient Implementations of the Generalized Lasso Dual Path Algorithm | We consider efficient implementations of the generalized lasso dual path
algorithm of Tibshirani and Taylor (2011). We first describe a generic approach
that covers any penalty matrix D and any (full column rank) matrix X of
predictor variables. We then describe fast implementations for the special
cases of trend filtering problems, fused lasso problems, and sparse fused lasso
problems, both with X=I and a general matrix X. These specialized
implementations offer a considerable improvement over the generic
implementation, both in terms of numerical stability and efficiency of the
solution path computation. These algorithms are all available for use in the
genlasso R package, which can be found in the CRAN repository.
| [
"['Taylor Arnold' 'Ryan Tibshirani']",
"Taylor Arnold and Ryan Tibshirani"
]
|
math.ST cs.LG stat.ML stat.TH | null | 1405.3224 | null | null | http://arxiv.org/pdf/1405.3224v2 | 2015-02-24T08:55:57Z | 2014-05-13T16:47:17Z | On the Complexity of A/B Testing | A/B testing refers to the task of determining the best option among two
alternatives that yield random outcomes. We provide distribution-dependent
lower bounds for the performance of A/B testing that improve over the results
currently available both in the fixed-confidence (or delta-PAC) and
fixed-budget settings. When the distribution of the outcomes are Gaussian, we
prove that the complexity of the fixed-confidence and fixed-budget settings are
equivalent, and that uniform sampling of both alternatives is optimal only in
the case of equal variances. In the common variance case, we also provide a
stopping rule that terminates faster than existing fixed-confidence algorithms.
In the case of Bernoulli distributions, we show that the complexity of
fixed-budget setting is smaller than that of fixed-confidence setting and that
uniform sampling of both alternatives -though not optimal- is advisable in
practice when combined with an appropriate stopping criterion.
| [
"['Emilie Kaufmann' 'Olivier Cappé' 'Aurélien Garivier']",
"Emilie Kaufmann (LTCI), Olivier Capp\\'e (LTCI), Aur\\'elien Garivier\n (IMT)"
]
|
cs.LG cs.AI math.OC math.ST stat.TH | null | 1405.3229 | null | null | http://arxiv.org/pdf/1405.3229v1 | 2014-05-13T16:51:54Z | 2014-05-13T16:51:54Z | Rate of Convergence and Error Bounds for LSTD($\lambda$) | We consider LSTD($\lambda$), the least-squares temporal-difference algorithm
with eligibility traces algorithm proposed by Boyan (2002). It computes a
linear approximation of the value function of a fixed policy in a large Markov
Decision Process. Under a $\beta$-mixing assumption, we derive, for any value
of $\lambda \in (0,1)$, a high-probability estimate of the rate of convergence
of this algorithm to its limit. We deduce a high-probability bound on the error
of this algorithm, that extends (and slightly improves) that derived by Lazaric
et al. (2012) in the specific case where $\lambda=0$. In particular, our
analysis sheds some light on the choice of $\lambda$ with respect to the
quality of the chosen linear space and the number of samples, that complies
with simulations.
| [
"Manel Tagorti (INRIA Nancy - Grand Est / LORIA), Bruno Scherrer (INRIA\n Nancy - Grand Est / LORIA)",
"['Manel Tagorti' 'Bruno Scherrer']"
]
|
stat.ME cs.LG | 10.1002/sam.11206 | 1405.3292 | null | null | http://arxiv.org/abs/1405.3292v1 | 2014-05-13T20:03:14Z | 2014-05-13T20:03:14Z | Learning with many experts: model selection and sparsity | Experts classifying data are often imprecise. Recently, several models have
been proposed to train classifiers using the noisy labels generated by these
experts. How to choose between these models? In such situations, the true
labels are unavailable. Thus, one cannot perform model selection using the
standard versions of methods such as empirical risk minimization and cross
validation. In order to allow model selection, we present a surrogate loss and
provide theoretical guarantees that assure its consistency. Next, we discuss
how this loss can be used to tune a penalization which introduces sparsity in
the parameters of a traditional class of models. Sparsity provides more
parsimonious models and can avoid overfitting. Nevertheless, it has seldom been
discussed in the context of noisy labels due to the difficulty in model
selection and, therefore, in choosing tuning parameters. We apply these
techniques to several sets of simulated and real data.
| [
"['Rafael Izbicki' 'Rafael Bassi Stern']",
"Rafael Izbicki, Rafael Bassi Stern"
]
|
stat.ML cs.LG stat.AP | null | 1405.3295 | null | null | http://arxiv.org/pdf/1405.3295v1 | 2014-05-13T20:07:09Z | 2014-05-13T20:07:09Z | Effects of Sampling Methods on Prediction Quality. The Case of
Classifying Land Cover Using Decision Trees | Clever sampling methods can be used to improve the handling of big data and
increase its usefulness. The subject of this study is remote sensing,
specifically airborne laser scanning point clouds representing different
classes of ground cover. The aim is to derive a supervised learning model for
the classification using CARTs. In order to measure the effect of different
sampling methods on the classification accuracy, various experiments with
varying types of sampling methods, sample sizes, and accuracy metrics have been
designed. Numerical results for a subset of a large surveying project covering
the lower Rhine area in Germany are shown. General conclusions regarding
sampling design are drawn and presented.
| [
"Ronald Hochreiter and Christoph Waldhauser",
"['Ronald Hochreiter' 'Christoph Waldhauser']"
]
|
cs.LG math.OC math.PR stat.ML | null | 1405.3316 | null | null | http://arxiv.org/pdf/1405.3316v2 | 2019-06-06T16:42:25Z | 2014-05-13T22:15:06Z | Optimal Exploration-Exploitation in a Multi-Armed-Bandit Problem with
Non-stationary Rewards | In a multi-armed bandit (MAB) problem a gambler needs to choose at each round
of play one of K arms, each characterized by an unknown reward distribution.
Reward realizations are only observed when an arm is selected, and the
gambler's objective is to maximize his cumulative expected earnings over some
given horizon of play T. To do this, the gambler needs to acquire information
about arms (exploration) while simultaneously optimizing immediate rewards
(exploitation); the price paid due to this trade off is often referred to as
the regret, and the main question is how small can this price be as a function
of the horizon length T. This problem has been studied extensively when the
reward distributions do not change over time; an assumption that supports a
sharp characterization of the regret, yet is often violated in practical
settings. In this paper, we focus on a MAB formulation which allows for a broad
range of temporal uncertainties in the rewards, while still maintaining
mathematical tractability. We fully characterize the (regret) complexity of
this class of MAB problems by establishing a direct link between the extent of
allowable reward "variation" and the minimal achievable regret. Our analysis
draws some connections between two rather disparate strands of literature: the
adversarial and the stochastic MAB frameworks.
| [
"Omar Besbes, Yonatan Gur, Assaf Zeevi",
"['Omar Besbes' 'Yonatan Gur' 'Assaf Zeevi']"
]
|
cs.AI cs.LG | null | 1405.3318 | null | null | http://arxiv.org/pdf/1405.3318v1 | 2014-05-13T22:29:14Z | 2014-05-13T22:29:14Z | Adaptive Monte Carlo via Bandit Allocation | We consider the problem of sequentially choosing between a set of unbiased
Monte Carlo estimators to minimize the mean-squared-error (MSE) of a final
combined estimate. By reducing this task to a stochastic multi-armed bandit
problem, we show that well developed allocation strategies can be used to
achieve an MSE that approaches that of the best estimator chosen in retrospect.
We then extend these developments to a scenario where alternative estimators
have different, possibly stochastic costs. The outcome is a new set of adaptive
Monte Carlo strategies that provide stronger guarantees than previous
approaches while offering practical advantages.
| [
"['James Neufeld' 'András György' 'Dale Schuurmans' 'Csaba Szepesvári']",
"James Neufeld, Andr\\'as Gy\\\"orgy, Dale Schuurmans, Csaba Szepesv\\'ari"
]
|
cs.CV cs.LG | null | 1405.3382 | null | null | http://arxiv.org/pdf/1405.3382v1 | 2014-05-14T07:00:38Z | 2014-05-14T07:00:38Z | Active Mining of Parallel Video Streams | The practicality of a video surveillance system is adversely limited by the
amount of queries that can be placed on human resources and their vigilance in
response. To transcend this limitation, a major effort under way is to include
software that (fully or at least semi) automatically mines video footage,
reducing the burden imposed to the system. Herein, we propose a semi-supervised
incremental learning framework for evolving visual streams in order to develop
a robust and flexible track classification system. Our proposed method learns
from consecutive batches by updating an ensemble in each time. It tries to
strike a balance between performance of the system and amount of data which
needs to be labelled. As no restriction is considered, the system can address
many practical problems in an evolving multi-camera scenario, such as concept
drift, class evolution and various length of video streams which have not been
addressed before. Experiments were performed on synthetic as well as real-world
visual data in non-stationary environments, showing high accuracy with fairly
little human collaboration.
| [
"Samaneh Khoshrou, Jaime S. Cardoso, Luis F. Teixeira",
"['Samaneh Khoshrou' 'Jaime S. Cardoso' 'Luis F. Teixeira']"
]
|
cs.LG | null | 1405.3396 | null | null | http://arxiv.org/pdf/1405.3396v1 | 2014-05-14T08:03:08Z | 2014-05-14T08:03:08Z | Reducing Dueling Bandits to Cardinal Bandits | We present algorithms for reducing the Dueling Bandits problem to the
conventional (stochastic) Multi-Armed Bandits problem. The Dueling Bandits
problem is an online model of learning with ordinal feedback of the form "A is
preferred to B" (as opposed to cardinal feedback like "A has value 2.5"),
giving it wide applicability in learning from implicit user feedback and
revealed and stated preferences. In contrast to existing algorithms for the
Dueling Bandits problem, our reductions -- named $\Doubler$, $\MultiSbm$ and
$\DoubleSbm$ -- provide a generic schema for translating the extensive body of
known results about conventional Multi-Armed Bandit algorithms to the Dueling
Bandits setting. For $\Doubler$ and $\MultiSbm$ we prove regret upper bounds in
both finite and infinite settings, and conjecture about the performance of
$\DoubleSbm$ which empirically outperforms the other two as well as previous
algorithms in our experiments. In addition, we provide the first almost optimal
regret bound in terms of second order terms, such as the differences between
the values of the arms.
| [
"['Nir Ailon' 'Thorsten Joachims' 'Zohar Karnin']",
"Nir Ailon and Thorsten Joachims and Zohar Karnin"
]
|
cs.LG cs.CR | 10.1016/j.eswa.2014.04.009 | 1405.3410 | null | null | http://arxiv.org/abs/1405.3410v1 | 2014-05-14T08:47:31Z | 2014-05-14T08:47:31Z | Efficient classification using parallel and scalable compressed model
and Its application on intrusion detection | In order to achieve high efficiency of classification in intrusion detection,
a compressed model is proposed in this paper which combines horizontal
compression with vertical compression. OneR is utilized as horizontal
com-pression for attribute reduction, and affinity propagation is employed as
vertical compression to select small representative exemplars from large
training data. As to be able to computationally compress the larger volume of
training data with scalability, MapReduce based parallelization approach is
then implemented and evaluated for each step of the model compression process
abovementioned, on which common but efficient classification methods can be
directly used. Experimental application study on two publicly available
datasets of intrusion detection, KDD99 and CMDC2012, demonstrates that the
classification using the compressed model proposed can effectively speed up the
detection procedure at up to 184 times, most importantly at the cost of a
minimal accuracy difference with less than 1% on average.
| [
"Tieming Chen, Xu Zhang, Shichao Jin, Okhee Kim",
"['Tieming Chen' 'Xu Zhang' 'Shichao Jin' 'Okhee Kim']"
]
|
stat.ML cs.LG | null | 1405.3536 | null | null | http://arxiv.org/pdf/1405.3536v1 | 2014-05-14T15:29:02Z | 2014-05-14T15:29:02Z | Improving offline evaluation of contextual bandit algorithms via
bootstrapping techniques | In many recommendation applications such as news recommendation, the items
that can be rec- ommended come and go at a very fast pace. This is a challenge
for recommender systems (RS) to face this setting. Online learning algorithms
seem to be the most straight forward solution. The contextual bandit framework
was introduced for that very purpose. In general the evaluation of a RS is a
critical issue. Live evaluation is of- ten avoided due to the potential loss of
revenue, hence the need for offline evaluation methods. Two options are
available. Model based meth- ods are biased by nature and are thus difficult to
trust when used alone. Data driven methods are therefore what we consider here.
Evaluat- ing online learning algorithms with past data is not simple but some
methods exist in the litera- ture. Nonetheless their accuracy is not satisfac-
tory mainly due to their mechanism of data re- jection that only allow the
exploitation of a small fraction of the data. We precisely address this issue
in this paper. After highlighting the limita- tions of the previous methods, we
present a new method, based on bootstrapping techniques. This new method comes
with two important improve- ments: it is much more accurate and it provides a
measure of quality of its estimation. The latter is a highly desirable property
in order to minimize the risks entailed by putting online a RS for the first
time. We provide both theoretical and ex- perimental proofs of its superiority
compared to state-of-the-art methods, as well as an analysis of the convergence
of the measure of quality.
| [
"Olivier Nicol (INRIA Lille - Nord Europe, LIFL), J\\'er\\'emie Mary\n (INRIA Lille - Nord Europe, LIFL), Philippe Preux (INRIA Lille - Nord Europe,\n LIFL)",
"['Olivier Nicol' 'Jérémie Mary' 'Philippe Preux']"
]
|
cs.SI cs.LG physics.soc-ph | 10.1371/journal.pcbi.1003892 | 1405.3612 | null | null | http://arxiv.org/abs/1405.3612v2 | 2014-07-15T16:11:43Z | 2014-05-14T18:26:23Z | Global disease monitoring and forecasting with Wikipedia | Infectious disease is a leading threat to public health, economic stability,
and other key social structures. Efforts to mitigate these impacts depend on
accurate and timely monitoring to measure the risk and progress of disease.
Traditional, biologically-focused monitoring techniques are accurate but costly
and slow; in response, new techniques based on social internet data such as
social media and search queries are emerging. These efforts are promising, but
important challenges in the areas of scientific peer review, breadth of
diseases and countries, and forecasting hamper their operational usefulness.
We examine a freely available, open data source for this use: access logs
from the online encyclopedia Wikipedia. Using linear models, language as a
proxy for location, and a systematic yet simple article selection procedure, we
tested 14 location-disease combinations and demonstrate that these data
feasibly support an approach that overcomes these challenges. Specifically, our
proof-of-concept yields models with $r^2$ up to 0.92, forecasting value up to
the 28 days tested, and several pairs of models similar enough to suggest that
transferring models from one location to another without re-training is
feasible.
Based on these preliminary results, we close with a research agenda designed
to overcome these challenges and produce a disease monitoring and forecasting
system that is significantly more effective, robust, and globally comprehensive
than the current state of the art.
| [
"['Nicholas Generous' 'Geoffrey Fairchild' 'Alina Deshpande'\n 'Sara Y. Del Valle' 'Reid Priedhorsky']",
"Nicholas Generous (1), Geoffrey Fairchild (1), Alina Deshpande (1),\n Sara Y. Del Valle (1), Reid Priedhorsky (1) ((1) Los Alamos National\n Laboratory, Los Alamos, NM)"
]
|
cs.SI cs.DC cs.IR cs.LG stat.ML | null | 1405.3726 | null | null | http://arxiv.org/pdf/1405.3726v1 | 2014-05-15T02:15:01Z | 2014-05-15T02:15:01Z | Topic words analysis based on LDA model | Social network analysis (SNA), which is a research field describing and
modeling the social connection of a certain group of people, is popular among
network services. Our topic words analysis project is a SNA method to visualize
the topic words among emails from Obama.com to accounts registered in Columbus,
Ohio. Based on Latent Dirichlet Allocation (LDA) model, a popular topic model
of SNA, our project characterizes the preference of senders for target group of
receptors. Gibbs sampling is used to estimate topic and word distribution. Our
training and testing data are emails from the carbon-free server
Datagreening.com. We use parallel computing tool BashReduce for word processing
and generate related words under each latent topic to discovers typical
information of political news sending specially to local Columbus receptors.
Running on two instances using paralleling tool BashReduce, our project
contributes almost 30% speedup processing the raw contents, comparing with
processing contents on one instance locally. Also, the experimental result
shows that the LDA model applied in our project provides precision rate 53.96%
higher than TF-IDF model finding target words, on the condition that
appropriate size of topic words list is selected.
| [
"Xi Qiu and Christopher Stewart",
"['Xi Qiu' 'Christopher Stewart']"
]
|
cs.LG | null | 1405.3843 | null | null | http://arxiv.org/pdf/1405.3843v1 | 2014-05-15T13:29:27Z | 2014-05-15T13:29:27Z | Logistic Regression: Tight Bounds for Stochastic and Online Optimization | The logistic loss function is often advocated in machine learning and
statistics as a smooth and strictly convex surrogate for the 0-1 loss. In this
paper we investigate the question of whether these smoothness and convexity
properties make the logistic loss preferable to other widely considered options
such as the hinge loss. We show that in contrast to known asymptotic bounds, as
long as the number of prediction/optimization iterations is sub exponential,
the logistic loss provides no improvement over a generic non-smooth loss
function such as the hinge loss. In particular we show that the convergence
rate of stochastic logistic optimization is bounded from below by a polynomial
in the diameter of the decision set and the number of prediction iterations,
and provide a matching tight upper bound. This resolves the COLT open problem
of McMahan and Streeter (2012).
| [
"Elad Hazan, Tomer Koren, Kfir Y. Levy",
"['Elad Hazan' 'Tomer Koren' 'Kfir Y. Levy']"
]
|
stat.ME cs.LG stat.ML | null | 1405.4047 | null | null | http://arxiv.org/pdf/1405.4047v2 | 2014-10-01T23:33:31Z | 2014-05-16T01:30:11Z | Methods and Models for Interpretable Linear Classification | We present an integer programming framework to build accurate and
interpretable discrete linear classification models. Unlike existing
approaches, our framework is designed to provide practitioners with the control
and flexibility they need to tailor accurate and interpretable models for a
domain of choice. To this end, our framework can produce models that are fully
optimized for accuracy, by minimizing the 0--1 classification loss, and that
address multiple aspects of interpretability, by incorporating a range of
discrete constraints and penalty functions. We use our framework to produce
models that are difficult to create with existing methods, such as scoring
systems and M-of-N rule tables. In addition, we propose specially designed
optimization methods to improve the scalability of our framework through
decomposition and data reduction. We show that discrete linear classifiers can
attain the training accuracy of any other linear classifier, and provide an
Occam's Razor type argument as to why the use of small discrete coefficients
can provide better generalization. We demonstrate the performance and
flexibility of our framework through numerical experiments and a case study in
which we construct a highly tailored clinical tool for sleep apnea diagnosis.
| [
"Berk Ustun and Cynthia Rudin",
"['Berk Ustun' 'Cynthia Rudin']"
]
|
cs.CL cs.AI cs.LG | null | 1405.4053 | null | null | http://arxiv.org/pdf/1405.4053v2 | 2014-05-22T23:23:19Z | 2014-05-16T07:12:16Z | Distributed Representations of Sentences and Documents | Many machine learning algorithms require the input to be represented as a
fixed-length feature vector. When it comes to texts, one of the most common
fixed-length features is bag-of-words. Despite their popularity, bag-of-words
features have two major weaknesses: they lose the ordering of the words and
they also ignore semantics of the words. For example, "powerful," "strong" and
"Paris" are equally distant. In this paper, we propose Paragraph Vector, an
unsupervised algorithm that learns fixed-length feature representations from
variable-length pieces of texts, such as sentences, paragraphs, and documents.
Our algorithm represents each document by a dense vector which is trained to
predict words in the document. Its construction gives our algorithm the
potential to overcome the weaknesses of bag-of-words models. Empirical results
show that Paragraph Vectors outperform bag-of-words models as well as other
techniques for text representations. Finally, we achieve new state-of-the-art
results on several text classification and sentiment analysis tasks.
| [
"Quoc V. Le and Tomas Mikolov",
"['Quoc V. Le' 'Tomas Mikolov']"
]
|
cs.LG stat.ML | null | 1405.4324 | null | null | http://arxiv.org/pdf/1405.4324v1 | 2014-05-16T22:31:42Z | 2014-05-16T22:31:42Z | Active Semi-Supervised Learning Using Sampling Theory for Graph Signals | We consider the problem of offline, pool-based active semi-supervised
learning on graphs. This problem is important when the labeled data is scarce
and expensive whereas unlabeled data is easily available. The data points are
represented by the vertices of an undirected graph with the similarity between
them captured by the edge weights. Given a target number of nodes to label, the
goal is to choose those nodes that are most informative and then predict the
unknown labels. We propose a novel framework for this problem based on our
recent results on sampling theory for graph signals. A graph signal is a
real-valued function defined on each node of the graph. A notion of frequency
for such signals can be defined using the spectrum of the graph Laplacian
matrix. The sampling theory for graph signals aims to extend the traditional
Nyquist-Shannon sampling theory by allowing us to identify the class of graph
signals that can be reconstructed from their values on a subset of vertices.
This approach allows us to define a criterion for active learning based on
sampling set selection which aims at maximizing the frequency of the signals
that can be reconstructed from their samples on the set. Experiments show the
effectiveness of our method.
| [
"Akshay Gadde, Aamir Anis and Antonio Ortega",
"['Akshay Gadde' 'Aamir Anis' 'Antonio Ortega']"
]
|
cs.LG cs.CE q-bio.QM stat.ML | null | 1405.4394 | null | null | http://arxiv.org/pdf/1405.4394v1 | 2014-05-17T13:51:42Z | 2014-05-17T13:51:42Z | Identification of functionally related enzymes by learning-to-rank
methods | Enzyme sequences and structures are routinely used in the biological sciences
as queries to search for functionally related enzymes in online databases. To
this end, one usually departs from some notion of similarity, comparing two
enzymes by looking for correspondences in their sequences, structures or
surfaces. For a given query, the search operation results in a ranking of the
enzymes in the database, from very similar to dissimilar enzymes, while
information about the biological function of annotated database enzymes is
ignored.
In this work we show that rankings of that kind can be substantially improved
by applying kernel-based learning algorithms. This approach enables the
detection of statistical dependencies between similarities of the active cleft
and the biological function of annotated enzymes. This is in contrast to
search-based approaches, which do not take annotated training data into
account. Similarity measures based on the active cleft are known to outperform
sequence-based or structure-based measures under certain conditions. We
consider the Enzyme Commission (EC) classification hierarchy for obtaining
annotated enzymes during the training phase. The results of a set of sizeable
experiments indicate a consistent and significant improvement for a set of
similarity measures that exploit information about small cavities in the
surface of enzymes.
| [
"Michiel Stock, Thomas Fober, Eyke H\\\"ullermeier, Serghei Glinca,\n Gerhard Klebe, Tapio Pahikkala, Antti Airola, Bernard De Baets, Willem\n Waegeman",
"['Michiel Stock' 'Thomas Fober' 'Eyke Hüllermeier' 'Serghei Glinca'\n 'Gerhard Klebe' 'Tapio Pahikkala' 'Antti Airola' 'Bernard De Baets'\n 'Willem Waegeman']"
]
|
cs.LG | null | 1405.4423 | null | null | http://arxiv.org/pdf/1405.4423v1 | 2014-05-17T18:20:13Z | 2014-05-17T18:20:13Z | A two-step learning approach for solving full and almost full cold start
problems in dyadic prediction | Dyadic prediction methods operate on pairs of objects (dyads), aiming to
infer labels for out-of-sample dyads. We consider the full and almost full cold
start problem in dyadic prediction, a setting that occurs when both objects in
an out-of-sample dyad have not been observed during training, or if one of them
has been observed, but very few times. A popular approach for addressing this
problem is to train a model that makes predictions based on a pairwise feature
representation of the dyads, or, in case of kernel methods, based on a tensor
product pairwise kernel. As an alternative to such a kernel approach, we
introduce a novel two-step learning algorithm that borrows ideas from the
fields of pairwise learning and spectral filtering. We show theoretically that
the two-step method is very closely related to the tensor product kernel
approach, and experimentally that it yields a slightly better predictive
performance. Moreover, unlike existing tensor product kernel methods, the
two-step method allows closed-form solutions for training and parameter
selection via cross-validation estimates both in the full and almost full cold
start settings, making the approach much more efficient and straightforward to
implement.
| [
"['Tapio Pahikkala' 'Michiel Stock' 'Antti Airola' 'Tero Aittokallio'\n 'Bernard De Baets' 'Willem Waegeman']",
"Tapio Pahikkala, Michiel Stock, Antti Airola, Tero Aittokallio,\n Bernard De Baets, Willem Waegeman"
]
|
cs.NI cs.LG | 10.1109/COMST.2014.2320099 | 1405.4463 | null | null | http://arxiv.org/abs/1405.4463v2 | 2015-03-19T15:15:04Z | 2014-05-18T06:28:47Z | Machine Learning in Wireless Sensor Networks: Algorithms, Strategies,
and Applications | Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.
| [
"Mohammad Abu Alsheikh, Shaowei Lin, Dusit Niyato and Hwee-Pink Tan",
"['Mohammad Abu Alsheikh' 'Shaowei Lin' 'Dusit Niyato' 'Hwee-Pink Tan']"
]
|
cs.LG | null | 1405.4471 | null | null | http://arxiv.org/pdf/1405.4471v1 | 2014-05-18T08:47:58Z | 2014-05-18T08:47:58Z | Online Learning with Composite Loss Functions | We study a new class of online learning problems where each of the online
algorithm's actions is assigned an adversarial value, and the loss of the
algorithm at each step is a known and deterministic function of the values
assigned to its recent actions. This class includes problems where the
algorithm's loss is the minimum over the recent adversarial values, the maximum
over the recent values, or a linear combination of the recent values. We
analyze the minimax regret of this class of problems when the algorithm
receives bandit feedback, and prove that when the minimum or maximum functions
are used, the minimax regret is $\tilde \Omega(T^{2/3})$ (so called hard online
learning problems), and when a linear function is used, the minimax regret is
$\tilde O(\sqrt{T})$ (so called easy learning problems). Previously, the only
online learning problem that was known to be provably hard was the multi-armed
bandit with switching costs.
| [
"['Ofer Dekel' 'Jian Ding' 'Tomer Koren' 'Yuval Peres']",
"Ofer Dekel, Jian Ding, Tomer Koren, Yuval Peres"
]
|
cs.LG | null | 1405.4543 | null | null | http://arxiv.org/pdf/1405.4543v1 | 2014-05-18T19:54:18Z | 2014-05-18T19:54:18Z | A Distributed Algorithm for Training Nonlinear Kernel Machines | This paper concerns the distributed training of nonlinear kernel machines on
Map-Reduce. We show that a re-formulation of Nystr\"om approximation based
solution which is solved using gradient based techniques is well suited for
this, especially when it is necessary to work with a large number of basis
points. The main advantages of this approach are: avoidance of computing the
pseudo-inverse of the kernel sub-matrix corresponding to the basis points;
simplicity and efficiency of the distributed part of the computations; and,
friendliness to stage-wise addition of basis points. We implement the method
using an AllReduce tree on Hadoop and demonstrate its value on a few large
benchmark datasets.
| [
"Dhruv Mahajan, S. Sathiya Keerthi, S. Sundararajan",
"['Dhruv Mahajan' 'S. Sathiya Keerthi' 'S. Sundararajan']"
]
|
cs.LG | null | 1405.4544 | null | null | http://arxiv.org/pdf/1405.4544v2 | 2015-03-16T21:31:59Z | 2014-05-18T20:07:41Z | A distributed block coordinate descent method for training $l_1$
regularized linear classifiers | Distributed training of $l_1$ regularized classifiers has received great
attention recently. Most existing methods approach this problem by taking steps
obtained from approximating the objective by a quadratic approximation that is
decoupled at the individual variable level. These methods are designed for
multicore and MPI platforms where communication costs are low. They are
inefficient on systems such as Hadoop running on a cluster of commodity
machines where communication costs are substantial. In this paper we design a
distributed algorithm for $l_1$ regularization that is much better suited for
such systems than existing algorithms. A careful cost analysis is used to
support these points and motivate our method. The main idea of our algorithm is
to do block optimization of many variables on the actual objective function
within each computing node; this increases the computational cost per step that
is matched with the communication cost, and decreases the number of outer
iterations, thus yielding a faster overall method. Distributed Gauss-Seidel and
Gauss-Southwell greedy schemes are used for choosing variables to update in
each step. We establish global convergence theory for our algorithm, including
Q-linear rate of convergence. Experiments on two benchmark problems show our
method to be much faster than existing methods.
| [
"Dhruv Mahajan, S. Sathiya Keerthi, S. Sundararajan",
"['Dhruv Mahajan' 'S. Sathiya Keerthi' 'S. Sundararajan']"
]
|
cs.CV cs.LG | null | 1405.4583 | null | null | http://arxiv.org/pdf/1405.4583v1 | 2014-05-19T03:06:14Z | 2014-05-19T03:06:14Z | ESSP: An Efficient Approach to Minimizing Dense and Nonsubmodular Energy
Functions | Many recent advances in computer vision have demonstrated the impressive
power of dense and nonsubmodular energy functions in solving visual labeling
problems. However, minimizing such energies is challenging. None of existing
techniques (such as s-t graph cut, QPBO, BP and TRW-S) can individually do this
well. In this paper, we present an efficient method, namely ESSP, to optimize
binary MRFs with arbitrary pairwise potentials, which could be nonsubmodular
and with dense connectivity. We also provide a comparative study of our
approach and several recent promising methods. From our study, we make some
reasonable recommendations of combining existing methods that perform the best
in different situations for this challenging problem. Experimental results
validate that for dense and nonsubmodular energy functions, the proposed
approach can usually obtain lower energies than the best combination of other
techniques using comparably reasonable time.
| [
"['Wei Feng' 'Jiaya Jia' 'Zhi-Qiang Liu']",
"Wei Feng and Jiaya Jia and Zhi-Qiang Liu"
]
|
cs.NE cs.LG | null | 1405.4589 | null | null | http://arxiv.org/pdf/1405.4589v2 | 2014-05-20T11:53:39Z | 2014-05-19T03:50:21Z | A Parallel Way to Select the Parameters of SVM Based on the Ant
Optimization Algorithm | A large number of experimental data shows that Support Vector Machine (SVM)
algorithm has obvious advantages in text classification, handwriting
recognition, image classification, bioinformatics, and some other fields. To
some degree, the optimization of SVM depends on its kernel function and Slack
variable, the determinant of which is its parameters $\delta$ and c in the
classification function. That is to say,to optimize the SVM algorithm, the
optimization of the two parameters play a huge role. Ant Colony Optimization
(ACO) is optimization algorithm which simulate ants to find the optimal path.In
the available literature, we mix the ACO algorithm and Parallel algorithm
together to find a well parameters.
| [
"Chao Zhang, Hong-cen Mei, Hao Yang",
"['Chao Zhang' 'Hong-cen Mei' 'Hao Yang']"
]
|
cs.CL cs.LG stat.ML | null | 1405.4599 | null | null | http://arxiv.org/pdf/1405.4599v1 | 2014-05-19T04:36:38Z | 2014-05-19T04:36:38Z | Modelling Data Dispersion Degree in Automatic Robust Estimation for
Multivariate Gaussian Mixture Models with an Application to Noisy Speech
Processing | The trimming scheme with a prefixed cutoff portion is known as a method of
improving the robustness of statistical models such as multivariate Gaussian
mixture models (MG- MMs) in small scale tests by alleviating the impacts of
outliers. However, when this method is applied to real- world data, such as
noisy speech processing, it is hard to know the optimal cut-off portion to
remove the outliers and sometimes removes useful data samples as well. In this
paper, we propose a new method based on measuring the dispersion degree (DD) of
the training data to avoid this problem, so as to realise automatic robust
estimation for MGMMs. The DD model is studied by using two different measures.
For each one, we theoretically prove that the DD of the data samples in a
context of MGMMs approximately obeys a specific (chi or chi-square)
distribution. The proposed method is evaluated on a real-world application with
a moderately-sized speaker recognition task. Experiments show that the proposed
method can significantly improve the robustness of the conventional training
method of GMMs for speaker recognition.
| [
"['Dalei Wu' 'Haiqing Wu']",
"Dalei Wu and Haiqing Wu"
]
|
cs.LG cs.NE | null | 1405.4604 | null | null | http://arxiv.org/pdf/1405.4604v2 | 2014-05-28T03:05:00Z | 2014-05-19T04:56:30Z | On the saddle point problem for non-convex optimization | A central challenge to many fields of science and engineering involves
minimizing non-convex error functions over continuous, high dimensional spaces.
Gradient descent or quasi-Newton methods are almost ubiquitously used to
perform such minimizations, and it is often thought that a main source of
difficulty for the ability of these local methods to find the global minimum is
the proliferation of local minima with much higher error than the global
minimum. Here we argue, based on results from statistical physics, random
matrix theory, and neural network theory, that a deeper and more profound
difficulty originates from the proliferation of saddle points, not local
minima, especially in high dimensional problems of practical interest. Such
saddle points are surrounded by high error plateaus that can dramatically slow
down learning, and give the illusory impression of the existence of a local
minimum. Motivated by these arguments, we propose a new algorithm, the
saddle-free Newton method, that can rapidly escape high dimensional saddle
points, unlike gradient descent and quasi-Newton methods. We apply this
algorithm to deep neural network training, and provide preliminary numerical
evidence for its superior performance.
| [
"['Razvan Pascanu' 'Yann N. Dauphin' 'Surya Ganguli' 'Yoshua Bengio']",
"Razvan Pascanu, Yann N. Dauphin, Surya Ganguli and Yoshua Bengio"
]
|
cs.LG | null | 1405.4758 | null | null | http://arxiv.org/pdf/1405.4758v1 | 2014-05-19T14:56:51Z | 2014-05-19T14:56:51Z | Lipschitz Bandits: Regret Lower Bounds and Optimal Algorithms | We consider stochastic multi-armed bandit problems where the expected reward
is a Lipschitz function of the arm, and where the set of arms is either
discrete or continuous. For discrete Lipschitz bandits, we derive asymptotic
problem specific lower bounds for the regret satisfied by any algorithm, and
propose OSLB and CKL-UCB, two algorithms that efficiently exploit the Lipschitz
structure of the problem. In fact, we prove that OSLB is asymptotically
optimal, as its asymptotic regret matches the lower bound. The regret analysis
of our algorithms relies on a new concentration inequality for weighted sums of
KL divergences between the empirical distributions of rewards and their true
distributions. For continuous Lipschitz bandits, we propose to first discretize
the action space, and then apply OSLB or CKL-UCB, algorithms that provably
exploit the structure efficiently. This approach is shown, through numerical
experiments, to significantly outperform existing algorithms that directly deal
with the continuous set of arms. Finally the results and algorithms are
extended to contextual bandits with similarities.
| [
"['Stefan Magureanu' 'Richard Combes' 'Alexandre Proutiere']",
"Stefan Magureanu and Richard Combes and Alexandre Proutiere"
]
|
cs.LG cs.CV cs.IT math.IT math.OC stat.ML | null | 1405.4807 | null | null | http://arxiv.org/pdf/1405.4807v1 | 2014-05-19T16:58:24Z | 2014-05-19T16:58:24Z | Scalable Semidefinite Relaxation for Maximum A Posterior Estimation | Maximum a posteriori (MAP) inference over discrete Markov random fields is a
fundamental task spanning a wide spectrum of real-world applications, which is
known to be NP-hard for general graphs. In this paper, we propose a novel
semidefinite relaxation formulation (referred to as SDR) to estimate the MAP
assignment. Algorithmically, we develop an accelerated variant of the
alternating direction method of multipliers (referred to as SDPAD-LR) that can
effectively exploit the special structure of the new relaxation. Encouragingly,
the proposed procedure allows solving SDR for large-scale problems, e.g.,
problems on a grid graph comprising hundreds of thousands of variables with
multiple states per node. Compared with prior SDP solvers, SDPAD-LR is capable
of attaining comparable accuracy while exhibiting remarkably improved
scalability, in contrast to the commonly held belief that semidefinite
relaxation can only been applied on small-scale MRF problems. We have evaluated
the performance of SDR on various benchmark datasets including OPENGM2 and PIC
in terms of both the quality of the solutions and computation time.
Experimental results demonstrate that for a broad class of problems, SDPAD-LR
outperforms state-of-the-art algorithms in producing better MAP assignment in
an efficient manner.
| [
"['Qixing Huang' 'Yuxin Chen' 'Leonidas Guibas']",
"Qixing Huang, Yuxin Chen, and Leonidas Guibas"
]
|
cs.LG stat.ML | 10.1109/TPAMI.2016.2568185 | 1405.4897 | null | null | http://arxiv.org/abs/1405.4897v2 | 2016-08-21T22:04:31Z | 2014-05-19T21:07:08Z | Screening Tests for Lasso Problems | This paper is a survey of dictionary screening for the lasso problem. The
lasso problem seeks a sparse linear combination of the columns of a dictionary
to best match a given target vector. This sparse representation has proven
useful in a variety of subsequent processing and decision tasks. For a given
target vector, dictionary screening quickly identifies a subset of dictionary
columns that will receive zero weight in a solution of the corresponding lasso
problem. These columns can be removed from the dictionary prior to solving the
lasso problem without impacting the optimality of the solution obtained. This
has two potential advantages: it reduces the size of the dictionary, allowing
the lasso problem to be solved with less resources, and it may speed up
obtaining a solution. Using a geometrically intuitive framework, we provide
basic insights for understanding useful lasso screening tests and their
limitations. We also provide illustrative numerical studies on several
datasets.
| [
"Zhen James Xiang, Yun Wang and Peter J. Ramadge",
"['Zhen James Xiang' 'Yun Wang' 'Peter J. Ramadge']"
]
|
math.OC cs.CC cs.LG cs.NA stat.ML | null | 1405.4980 | null | null | http://arxiv.org/pdf/1405.4980v2 | 2015-11-16T18:52:04Z | 2014-05-20T07:50:56Z | Convex Optimization: Algorithms and Complexity | This monograph presents the main complexity theorems in convex optimization
and their corresponding algorithms. Starting from the fundamental theory of
black-box optimization, the material progresses towards recent advances in
structural optimization and stochastic optimization. Our presentation of
black-box optimization, strongly influenced by Nesterov's seminal book and
Nemirovski's lecture notes, includes the analysis of cutting plane methods, as
well as (accelerated) gradient descent schemes. We also pay special attention
to non-Euclidean settings (relevant algorithms include Frank-Wolfe, mirror
descent, and dual averaging) and discuss their relevance in machine learning.
We provide a gentle introduction to structural optimization with FISTA (to
optimize a sum of a smooth and a simple non-smooth term), saddle-point mirror
prox (Nemirovski's alternative to Nesterov's smoothing), and a concise
description of interior point methods. In stochastic optimization we discuss
stochastic gradient descent, mini-batches, random coordinate descent, and
sublinear algorithms. We also briefly touch upon convex relaxation of
combinatorial problems and the use of randomness to round solutions, as well as
random walks based methods.
| [
"['Sébastien Bubeck']",
"S\\'ebastien Bubeck"
]
|
cs.LG stat.ML | null | 1405.5096 | null | null | http://arxiv.org/pdf/1405.5096v1 | 2014-05-20T14:15:54Z | 2014-05-20T14:15:54Z | Unimodal Bandits: Regret Lower Bounds and Optimal Algorithms | We consider stochastic multi-armed bandits where the expected reward is a
unimodal function over partially ordered arms. This important class of problems
has been recently investigated in (Cope 2009, Yu 2011). The set of arms is
either discrete, in which case arms correspond to the vertices of a finite
graph whose structure represents similarity in rewards, or continuous, in which
case arms belong to a bounded interval. For discrete unimodal bandits, we
derive asymptotic lower bounds for the regret achieved under any algorithm, and
propose OSUB, an algorithm whose regret matches this lower bound. Our algorithm
optimally exploits the unimodal structure of the problem, and surprisingly, its
asymptotic regret does not depend on the number of arms. We also provide a
regret upper bound for OSUB in non-stationary environments where the expected
rewards smoothly evolve over time. The analytical results are supported by
numerical experiments showing that OSUB performs significantly better than the
state-of-the-art algorithms. For continuous sets of arms, we provide a brief
discussion. We show that combining an appropriate discretization of the set of
arms with the UCB algorithm yields an order-optimal regret, and in practice,
outperforms recently proposed algorithms designed to exploit the unimodal
structure.
| [
"['Richard Combes' 'Alexandre Proutiere']",
"Richard Combes and Alexandre Proutiere"
]
|
cs.LG cs.IR | null | 1405.5147 | null | null | http://arxiv.org/pdf/1405.5147v1 | 2014-05-20T16:32:59Z | 2014-05-20T16:32:59Z | Predicting Online Video Engagement Using Clickstreams | In the nascent days of e-content delivery, having a superior product was
enough to give companies an edge against the competition. With today's fiercely
competitive market, one needs to be multiple steps ahead, especially when it
comes to understanding consumers. Focusing on a large set of web portals owned
and managed by a private communications company, we propose methods by which
these sites' clickstream data can be used to provide a deep understanding of
their visitors, as well as their interests and preferences. We further expand
the use of this data to show that it can be effectively used to predict user
engagement to video streams.
| [
"['Everaldo Aguiar' 'Saurabh Nagrecha' 'Nitesh V. Chawla']",
"Everaldo Aguiar, Saurabh Nagrecha, Nitesh V. Chawla"
]
|
cs.LG cs.AI stat.ML | null | 1405.5156 | null | null | http://arxiv.org/pdf/1405.5156v1 | 2014-05-20T17:12:56Z | 2014-05-20T17:12:56Z | Gaussian Approximation of Collective Graphical Models | The Collective Graphical Model (CGM) models a population of independent and
identically distributed individuals when only collective statistics (i.e.,
counts of individuals) are observed. Exact inference in CGMs is intractable,
and previous work has explored Markov Chain Monte Carlo (MCMC) and MAP
approximations for learning and inference. This paper studies Gaussian
approximations to the CGM. As the population grows large, we show that the CGM
distribution converges to a multivariate Gaussian distribution (GCGM) that
maintains the conditional independence properties of the original CGM. If the
observations are exact marginals of the CGM or marginals that are corrupted by
Gaussian noise, inference in the GCGM approximation can be computed efficiently
in closed form. If the observations follow a different noise model (e.g.,
Poisson), then expectation propagation provides efficient and accurate
approximate inference. The accuracy and speed of GCGM inference is compared to
the MCMC and MAP methods on a simulated bird migration problem. The GCGM
matches or exceeds the accuracy of the MAP method while being significantly
faster.
| [
"Li-Ping Liu, Daniel Sheldon, Thomas G. Dietterich",
"['Li-Ping Liu' 'Daniel Sheldon' 'Thomas G. Dietterich']"
]
|
cs.LG cs.CC cs.DM | null | 1405.5268 | null | null | http://arxiv.org/pdf/1405.5268v2 | 2014-07-09T19:16:57Z | 2014-05-21T00:06:02Z | Approximate resilience, monotonicity, and the complexity of agnostic
learning | A function $f$ is $d$-resilient if all its Fourier coefficients of degree at
most $d$ are zero, i.e., $f$ is uncorrelated with all low-degree parities. We
study the notion of $\mathit{approximate}$ $\mathit{resilience}$ of Boolean
functions, where we say that $f$ is $\alpha$-approximately $d$-resilient if $f$
is $\alpha$-close to a $[-1,1]$-valued $d$-resilient function in $\ell_1$
distance. We show that approximate resilience essentially characterizes the
complexity of agnostic learning of a concept class $C$ over the uniform
distribution. Roughly speaking, if all functions in a class $C$ are far from
being $d$-resilient then $C$ can be learned agnostically in time $n^{O(d)}$ and
conversely, if $C$ contains a function close to being $d$-resilient then
agnostic learning of $C$ in the statistical query (SQ) framework of Kearns has
complexity of at least $n^{\Omega(d)}$. This characterization is based on the
duality between $\ell_1$ approximation by degree-$d$ polynomials and
approximate $d$-resilience that we establish. In particular, it implies that
$\ell_1$ approximation by low-degree polynomials, known to be sufficient for
agnostic learning over product distributions, is in fact necessary.
Focusing on monotone Boolean functions, we exhibit the existence of
near-optimal $\alpha$-approximately
$\widetilde{\Omega}(\alpha\sqrt{n})$-resilient monotone functions for all
$\alpha>0$. Prior to our work, it was conceivable even that every monotone
function is $\Omega(1)$-far from any $1$-resilient function. Furthermore, we
construct simple, explicit monotone functions based on ${\sf Tribes}$ and ${\sf
CycleRun}$ that are close to highly resilient functions. Our constructions are
based on a fairly general resilience analysis and amplification. These
structural results, together with the characterization, imply nearly optimal
lower bounds for agnostic learning of monotone juntas.
| [
"['Dana Dachman-Soled' 'Vitaly Feldman' 'Li-Yang Tan' 'Andrew Wan'\n 'Karl Wimmer']",
"Dana Dachman-Soled and Vitaly Feldman and Li-Yang Tan and Andrew Wan\n and Karl Wimmer"
]
|
math.OC cs.LG | null | 1405.5300 | null | null | http://arxiv.org/pdf/1405.5300v2 | 2014-07-27T12:22:28Z | 2014-05-21T05:12:55Z | Fast Distributed Coordinate Descent for Non-Strongly Convex Losses | We propose an efficient distributed randomized coordinate descent method for
minimizing regularized non-strongly convex loss functions. The method attains
the optimal $O(1/k^2)$ convergence rate, where $k$ is the iteration counter.
The core of the work is the theoretical study of stepsize parameters. We have
implemented the method on Archer - the largest supercomputer in the UK - and
show that the method is capable of solving a (synthetic) LASSO optimization
problem with 50 billion variables.
| [
"Olivier Fercoq and Zheng Qu and Peter Richt\\'arik and Martin\n Tak\\'a\\v{c}",
"['Olivier Fercoq' 'Zheng Qu' 'Peter Richtárik' 'Martin Takáč']"
]
|
stat.ME cs.LG stat.ML | null | 1405.5311 | null | null | http://arxiv.org/pdf/1405.5311v1 | 2014-05-21T06:53:16Z | 2014-05-21T06:53:16Z | Compressive Sampling Using EM Algorithm | Conventional approaches of sampling signals follow the celebrated theorem of
Nyquist and Shannon. Compressive sampling, introduced by Donoho, Romberg and
Tao, is a new paradigm that goes against the conventional methods in data
acquisition and provides a way of recovering signals using fewer samples than
the traditional methods use. Here we suggest an alternative way of
reconstructing the original signals in compressive sampling using EM algorithm.
We first propose a naive approach which has certain computational difficulties
and subsequently modify it to a new approach which performs better than the
conventional methods of compressive sampling. The comparison of the different
approaches and the performance of the new approach has been studied using
simulated data.
| [
"['Atanu Kumar Ghosh' 'Arnab Chakraborty']",
"Atanu Kumar Ghosh, Arnab Chakraborty"
]
|
cs.AI cs.LG | null | 1405.5358 | null | null | http://arxiv.org/pdf/1405.5358v1 | 2014-05-21T10:20:15Z | 2014-05-21T10:20:15Z | Off-Policy Shaping Ensembles in Reinforcement Learning | Recent advances of gradient temporal-difference methods allow to learn
off-policy multiple value functions in parallel with- out sacrificing
convergence guarantees or computational efficiency. This opens up new
possibilities for sound ensemble techniques in reinforcement learning. In this
work we propose learning an ensemble of policies related through
potential-based shaping rewards. The ensemble induces a combination policy by
using a voting mechanism on its components. Learning happens in real time, and
we empirically show the combination policy to outperform the individual
policies of the ensemble.
| [
"Anna Harutyunyan and Tim Brys and Peter Vrancx and Ann Nowe",
"['Anna Harutyunyan' 'Tim Brys' 'Peter Vrancx' 'Ann Nowe']"
]
|
cs.CV cs.LG | null | 1405.5488 | null | null | http://arxiv.org/pdf/1405.5488v1 | 2014-04-24T02:29:19Z | 2014-04-24T02:29:19Z | On Learning Where To Look | Current automatic vision systems face two major challenges: scalability and
extreme variability of appearance. First, the computational time required to
process an image typically scales linearly with the number of pixels in the
image, therefore limiting the resolution of input images to thumbnail size.
Second, variability in appearance and pose of the objects constitute a major
hurdle for robust recognition and detection. In this work, we propose a model
that makes baby steps towards addressing these challenges. We describe a
learning based method that recognizes objects through a series of glimpses.
This system performs an amount of computation that scales with the complexity
of the input rather than its number of pixels. Moreover, the proposed method is
potentially more robust to changes in appearance since its parameters are
learned in a data driven manner. Preliminary experiments on a handwritten
dataset of digits demonstrate the computational advantages of this approach.
| [
"[\"Marc'Aurelio Ranzato\"]",
"Marc'Aurelio Ranzato"
]
|
stat.ML cs.LG | null | 1405.5505 | null | null | http://arxiv.org/pdf/1405.5505v3 | 2016-02-25T09:28:14Z | 2014-05-21T18:17:37Z | Kernel Mean Shrinkage Estimators | A mean function in a reproducing kernel Hilbert space (RKHS), or a kernel
mean, is central to kernel methods in that it is used by many classical
algorithms such as kernel principal component analysis, and it also forms the
core inference step of modern kernel methods that rely on embedding probability
distributions in RKHSs. Given a finite sample, an empirical average has been
used commonly as a standard estimator of the true kernel mean. Despite a
widespread use of this estimator, we show that it can be improved thanks to the
well-known Stein phenomenon. We propose a new family of estimators called
kernel mean shrinkage estimators (KMSEs), which benefit from both theoretical
justifications and good empirical performance. The results demonstrate that the
proposed estimators outperform the standard one, especially in a "large d,
small n" paradigm.
| [
"Krikamol Muandet, Bharath Sriperumbudur, Kenji Fukumizu, Arthur\n Gretton, Bernhard Sch\\\"olkopf",
"['Krikamol Muandet' 'Bharath Sriperumbudur' 'Kenji Fukumizu'\n 'Arthur Gretton' 'Bernhard Schölkopf']"
]
|
cs.CV cs.LG | null | 1405.5769 | null | null | http://arxiv.org/pdf/1405.5769v2 | 2015-06-24T09:16:28Z | 2014-05-22T14:35:52Z | Descriptor Matching with Convolutional Neural Networks: a Comparison to
SIFT | Latest results indicate that features learned via convolutional neural
networks outperform previous descriptors on classification tasks by a large
margin. It has been shown that these networks still work well when they are
applied to datasets or recognition tasks different from those they were trained
on. However, descriptors like SIFT are not only used in recognition but also
for many correspondence problems that rely on descriptor matching. In this
paper we compare features from various layers of convolutional neural nets to
standard SIFT descriptors. We consider a network that was trained on ImageNet
and another one that was trained without supervision. Surprisingly,
convolutional neural networks clearly outperform SIFT on descriptor matching.
This paper has been merged with arXiv:1406.6909
| [
"Philipp Fischer, Alexey Dosovitskiy, Thomas Brox",
"['Philipp Fischer' 'Alexey Dosovitskiy' 'Thomas Brox']"
]
|
cs.DB cs.LG | null | 1405.5829 | null | null | http://arxiv.org/pdf/1405.5829v1 | 2014-05-22T17:13:00Z | 2014-05-22T17:13:00Z | Node Classification in Uncertain Graphs | In many real applications that use and analyze networked data, the links in
the network graph may be erroneous, or derived from probabilistic techniques.
In such cases, the node classification problem can be challenging, since the
unreliability of the links may affect the final results of the classification
process. If the information about link reliability is not used explicitly, the
classification accuracy in the underlying network may be affected adversely. In
this paper, we focus on situations that require the analysis of the uncertainty
that is present in the graph structure. We study the novel problem of node
classification in uncertain graphs, by treating uncertainty as a first-class
citizen. We propose two techniques based on a Bayes model and automatic
parameter selection, and show that the incorporation of uncertainty in the
classification process as a first-class citizen is beneficial. We
experimentally evaluate the proposed approach using different real data sets,
and study the behavior of the algorithms under different conditions. The
results demonstrate the effectiveness and efficiency of our approach.
| [
"['Michele Dallachiesa' 'Charu Aggarwal' 'Themis Palpanas']",
"Michele Dallachiesa and Charu Aggarwal and Themis Palpanas"
]
|
cs.LG cs.SI physics.soc-ph | null | 1405.5868 | null | null | http://arxiv.org/pdf/1405.5868v2 | 2014-11-10T18:11:10Z | 2014-05-22T19:41:51Z | Learning to Generate Networks | We investigate the problem of learning to generate complex networks from
data. Specifically, we consider whether deep belief networks, dependency
networks, and members of the exponential random graph family can learn to
generate networks whose complex behavior is consistent with a set of input
examples. We find that the deep model is able to capture the complex behavior
of small networks, but that no model is able capture this behavior for networks
with more than a handful of nodes.
| [
"James Atwood, Don Towsley, Krista Gile, and David Jensen",
"['James Atwood' 'Don Towsley' 'Krista Gile' 'David Jensen']"
]
|
stat.ML cs.DS cs.IR cs.LG | null | 1405.5869 | null | null | http://arxiv.org/pdf/1405.5869v1 | 2014-05-22T19:42:57Z | 2014-05-22T19:42:57Z | Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search
(MIPS) | We present the first provably sublinear time algorithm for approximate
\emph{Maximum Inner Product Search} (MIPS). Our proposal is also the first
hashing algorithm for searching with (un-normalized) inner product as the
underlying similarity measure. Finding hashing schemes for MIPS was considered
hard. We formally show that the existing Locality Sensitive Hashing (LSH)
framework is insufficient for solving MIPS, and then we extend the existing LSH
framework to allow asymmetric hashing schemes. Our proposal is based on an
interesting mathematical phenomenon in which inner products, after independent
asymmetric transformations, can be converted into the problem of approximate
near neighbor search. This key observation makes efficient sublinear hashing
scheme for MIPS possible. In the extended asymmetric LSH (ALSH) framework, we
provide an explicit construction of provably fast hashing scheme for MIPS. The
proposed construction and the extended LSH framework could be of independent
theoretical interest. Our proposed algorithm is simple and easy to implement.
We evaluate the method, for retrieving inner products, in the collaborative
filtering task of item recommendations on Netflix and Movielens datasets.
| [
"['Anshumali Shrivastava' 'Ping Li']",
"Anshumali Shrivastava and Ping Li"
]
|
cs.LG math.OC stat.ML | null | 1405.5960 | null | null | http://arxiv.org/pdf/1405.5960v1 | 2014-05-23T04:28:29Z | 2014-05-23T04:28:29Z | LASS: a simple assignment model with Laplacian smoothing | We consider the problem of learning soft assignments of $N$ items to $K$
categories given two sources of information: an item-category similarity
matrix, which encourages items to be assigned to categories they are similar to
(and to not be assigned to categories they are dissimilar to), and an item-item
similarity matrix, which encourages similar items to have similar assignments.
We propose a simple quadratic programming model that captures this intuition.
We give necessary conditions for its solution to be unique, define an
out-of-sample mapping, and derive a simple, effective training algorithm based
on the alternating direction method of multipliers. The model predicts
reasonable assignments from even a few similarity values, and can be seen as a
generalization of semisupervised learning. It is particularly useful when items
naturally belong to multiple categories, as for example when annotating
documents with keywords or pictures with tags, with partially tagged items, or
when the categories have complex interrelations (e.g. hierarchical) that are
unknown.
| [
"Miguel \\'A. Carreira-Perpi\\~n\\'an and Weiran Wang",
"['Miguel Á. Carreira-Perpiñán' 'Weiran Wang']"
]
|
cs.CV cs.LG stat.ML | null | 1405.6012 | null | null | http://arxiv.org/pdf/1405.6012v1 | 2014-05-23T10:15:04Z | 2014-05-23T10:15:04Z | On the Optimal Solution of Weighted Nuclear Norm Minimization | In recent years, the nuclear norm minimization (NNM) problem has been
attracting much attention in computer vision and machine learning. The NNM
problem is capitalized on its convexity and it can be solved efficiently. The
standard nuclear norm regularizes all singular values equally, which is however
not flexible enough to fit real scenarios. Weighted nuclear norm minimization
(WNNM) is a natural extension and generalization of NNM. By assigning properly
different weights to different singular values, WNNM can lead to
state-of-the-art results in applications such as image denoising. Nevertheless,
so far the global optimal solution of WNNM problem is not completely solved yet
due to its non-convexity in general cases. In this article, we study the
theoretical properties of WNNM and prove that WNNM can be equivalently
transformed into a quadratic programming problem with linear constraints. This
implies that WNNM is equivalent to a convex problem and its global optimum can
be readily achieved by off-the-shelf convex optimization solvers. We further
show that when the weights are non-descending, the globally optimal solution of
WNNM can be obtained in closed-form.
| [
"Qi Xie, Deyu Meng, Shuhang Gu, Lei Zhang, Wangmeng Zuo, Xiangchu Feng\n and Zongben Xu",
"['Qi Xie' 'Deyu Meng' 'Shuhang Gu' 'Lei Zhang' 'Wangmeng Zuo'\n 'Xiangchu Feng' 'Zongben Xu']"
]
|
cs.LG | null | 1405.6076 | null | null | http://arxiv.org/pdf/1405.6076v1 | 2014-05-23T14:33:48Z | 2014-05-23T14:33:48Z | Online Linear Optimization via Smoothing | We present a new optimization-theoretic approach to analyzing
Follow-the-Leader style algorithms, particularly in the setting where
perturbations are used as a tool for regularization. We show that adding a
strongly convex penalty function to the decision rule and adding stochastic
perturbations to data correspond to deterministic and stochastic smoothing
operations, respectively. We establish an equivalence between "Follow the
Regularized Leader" and "Follow the Perturbed Leader" up to the smoothness
properties. This intuition leads to a new generic analysis framework that
recovers and improves the previous known regret bounds of the class of
algorithms commonly known as Follow the Perturbed Leader.
| [
"['Jacob Abernethy' 'Chansoo Lee' 'Abhinav Sinha' 'Ambuj Tewari']",
"Jacob Abernethy, Chansoo Lee, Abhinav Sinha, Ambuj Tewari"
]
|
cs.CV cs.LG cs.NE | null | 1405.6137 | null | null | http://arxiv.org/pdf/1405.6137v1 | 2014-02-05T20:05:34Z | 2014-02-05T20:05:34Z | An enhanced neural network based approach towards object extraction | The improvements in spectral and spatial resolution of the satellite images
have facilitated the automatic extraction and identification of the features
from satellite images and aerial photographs. An automatic object extraction
method is presented for extracting and identifying the various objects from
satellite images and the accuracy of the system is verified with regard to IRS
satellite images. The system is based on neural network and simulates the
process of visual interpretation from remote sensing images and hence increases
the efficiency of image analysis. This approach obtains the basic
characteristics of the various features and the performance is enhanced by the
automatic learning approach, intelligent interpretation, and intelligent
interpolation. The major advantage of the method is its simplicity and that the
system identifies the features not only based on pixel value but also based on
the shape, haralick features etc of the objects. Further the system allows
flexibility for identifying the features within the same category based on size
and shape. The successful application of the system verified its effectiveness
and the accuracy of the system were assessed by ground truth verification.
| [
"['S. K. Katiyar' 'P. V. Arun']",
"S.K. Katiyar and P.V. Arun"
]
|
cs.CV cs.LG stat.ML | 10.1137/140967325 | 1405.6159 | null | null | http://arxiv.org/abs/1405.6159v3 | 2014-08-20T22:12:15Z | 2014-04-30T21:58:10Z | A Bi-clustering Framework for Consensus Problems | We consider grouping as a general characterization for problems such as
clustering, community detection in networks, and multiple parametric model
estimation. We are interested in merging solutions from different grouping
algorithms, distilling all their good qualities into a consensus solution. In
this paper, we propose a bi-clustering framework and perspective for reaching
consensus in such grouping problems. In particular, this is the first time that
the task of finding/fitting multiple parametric models to a dataset is formally
posed as a consensus problem. We highlight the equivalence of these tasks and
establish the connection with the computational Gestalt program, that seeks to
provide a psychologically-inspired detection theory for visual events. We also
present a simple but powerful bi-clustering algorithm, specially tuned to the
nature of the problem we address, though general enough to handle many
different instances inscribed within our characterization. The presentation is
accompanied with diverse and extensive experimental results in clustering,
community detection, and multiple parametric model estimation in image
processing applications.
| [
"Mariano Tepper and Guillermo Sapiro",
"['Mariano Tepper' 'Guillermo Sapiro']"
]
|
cs.CV cs.LG | 10.5121/ijfcst.2014.4102 | 1405.6177 | null | null | http://arxiv.org/abs/1405.6177v1 | 2014-02-14T20:53:43Z | 2014-02-14T20:53:43Z | Automated Fabric Defect Inspection: A Survey of Classifiers | Quality control at each stage of production in textile industry has become a
key factor to retaining the existence in the highly competitive global market.
Problems of manual fabric defect inspection are lack of accuracy and high time
consumption, where early and accurate fabric defect detection is a significant
phase of quality control. Computer vision based, i.e. automated fabric defect
inspection systems are thought by many researchers of different countries to be
very useful to resolve these problems. There are two major challenges to be
resolved to attain a successful automated fabric defect inspection system. They
are defect detection and defect classification. In this work, we discuss
different techniques used for automated fabric defect classification, then show
a survey of classifiers used in automated fabric defect inspection systems, and
finally, compare these classifiers by using performance metrics. This work is
expected to be very useful for the researchers in the area of automated fabric
defect inspection to understand and evaluate the many potential options in this
field.
| [
"['Md. Tarek Habib' 'Rahat Hossain Faisal' 'M. Rokonuzzaman' 'Farruk Ahmed']",
"Md. Tarek Habib, Rahat Hossain Faisal, M. Rokonuzzaman, Farruk Ahmed"
]
|
cs.LG cs.IR | null | 1405.6223 | null | null | http://arxiv.org/pdf/1405.6223v1 | 2014-04-08T00:42:16Z | 2014-04-08T00:42:16Z | Coupled Item-based Matrix Factorization | The essence of the challenges cold start and sparsity in Recommender Systems
(RS) is that the extant techniques, such as Collaborative Filtering (CF) and
Matrix Factorization (MF), mainly rely on the user-item rating matrix, which
sometimes is not informative enough for predicting recommendations. To solve
these challenges, the objective item attributes are incorporated as
complementary information. However, most of the existing methods for inferring
the relationships between items assume that the attributes are "independently
and identically distributed (iid)", which does not always hold in reality. In
fact, the attributes are more or less coupled with each other by some implicit
relationships. Therefore, in this pa-per we propose an attribute-based coupled
similarity measure to capture the implicit relationships between items. We then
integrate the implicit item coupling into MF to form the Coupled Item-based
Matrix Factorization (CIMF) model. Experimental results on two open data sets
demonstrate that CIMF outperforms the benchmark methods.
| [
"Fangfang Li, Guandong Xu, Longbing Cao",
"['Fangfang Li' 'Guandong Xu' 'Longbing Cao']"
]
|
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