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Discriminative Parameter Estimation for Random Walks Segmentation | cs.CV cs.LG | The Random Walks (RW) algorithm is one of the most e - cient and easy-to-use
probabilistic segmentation methods. By combining contrast terms with prior
terms, it provides accurate segmentations of medical images in a fully
automated manner. However, one of the main drawbacks of using the RW algorithm
is that its parameters have to be hand-tuned. we propose a novel discriminative
learning framework that estimates the parameters using a training dataset. The
main challenge we face is that the training samples are not fully supervised.
Speci cally, they provide a hard segmentation of the images, instead of a
proba- bilistic segmentation. We overcome this challenge by treating the opti-
mal probabilistic segmentation that is compatible with the given hard
segmentation as a latent variable. This allows us to employ the latent support
vector machine formulation for parameter estimation. We show that our approach
signi cantly outperforms the baseline methods on a challenging dataset
consisting of real clinical 3D MRI volumes of skeletal muscles.
| Pierre-Yves Baudin (INRIA Saclay - Ile de France), Danny Goodman,
Puneet Kumar (INRIA Saclay - Ile de France, CVN), Noura Azzabou (MIRCEN,
UPMC), Pierre G. Carlier (UPMC), Nikos Paragios (INRIA Saclay - Ile de
France, MAS, LIGM, ENPC), M. Pawan Kumar (INRIA Saclay - Ile de France, CVN) | null | 1308.6721 | null | null |
Preventing Disclosure of Sensitive Knowledge by Hiding Inference | cs.CR cs.DB cs.LG | Data Mining is a way of extracting data or uncovering hidden patterns of
information from databases. So, there is a need to prevent the inference rules
from being disclosed such that the more secure data sets cannot be identified
from non sensitive attributes. This can be done through removing or adding
certain item sets in the transactions Sanitization. The purpose is to hide the
Inference rules, so that the user may not be able to discover any valuable
information from other non sensitive data and any organisation can release all
samples of their data without the fear of Knowledge Discovery In Databases
which can be achieved by investigating frequently occurring item sets, rules
that can be mined from them with the objective of hiding them. Another way is
to release only limited samples in the new database so that there is no
information loss and it also satisfies the legitimate needs of the users. The
major problem is uncovering hidden patterns, which causes a threat to the
database security. Sensitive data are inferred from non-sensitive data based on
the semantics of the application the user has, commonly known as the inference
problem. Two fundamental approaches to protect sensitive rules from disclosure
are that, preventing rules from being generated by hiding the frequent sets of
data items and reducing the importance of the rules by setting their confidence
below a user-specified threshold.
| A.S.Syed Navaz, M.Ravi and T.Prabhu | null | 1308.6744 | null | null |
Online Ranking: Discrete Choice, Spearman Correlation and Other Feedback | cs.LG cs.GT stat.ML | Given a set $V$ of $n$ objects, an online ranking system outputs at each time
step a full ranking of the set, observes a feedback of some form and suffers a
loss. We study the setting in which the (adversarial) feedback is an element in
$V$, and the loss is the position (0th, 1st, 2nd...) of the item in the
outputted ranking. More generally, we study a setting in which the feedback is
a subset $U$ of at most $k$ elements in $V$, and the loss is the sum of the
positions of those elements.
We present an algorithm of expected regret $O(n^{3/2}\sqrt{Tk})$ over a time
horizon of $T$ steps with respect to the best single ranking in hindsight. This
improves previous algorithms and analyses either by a factor of either
$\Omega(\sqrt{k})$, a factor of $\Omega(\sqrt{\log n})$ or by improving running
time from quadratic to $O(n\log n)$ per round. We also prove a matching lower
bound. Our techniques also imply an improved regret bound for online rank
aggregation over the Spearman correlation measure, and to other more complex
ranking loss functions.
| Nir Ailon | null | 1308.6797 | null | null |
Concentration Inequalities for Bounded Random Vectors | math.PR cs.LG math.ST stat.TH | We derive simple concentration inequalities for bounded random vectors, which
generalize Hoeffding's inequalities for bounded scalar random variables. As
applications, we apply the general results to multinomial and Dirichlet
distributions to obtain multivariate concentration inequalities.
| Xinjia Chen | null | 1309.0003 | null | null |
Non-Asymptotic Convergence Analysis of Inexact Gradient Methods for
Machine Learning Without Strong Convexity | math.OC cs.LG | Many recent applications in machine learning and data fitting call for the
algorithmic solution of structured smooth convex optimization problems.
Although the gradient descent method is a natural choice for this task, it
requires exact gradient computations and hence can be inefficient when the
problem size is large or the gradient is difficult to evaluate. Therefore,
there has been much interest in inexact gradient methods (IGMs), in which an
efficiently computable approximate gradient is used to perform the update in
each iteration. Currently, non-asymptotic linear convergence results for IGMs
are typically established under the assumption that the objective function is
strongly convex, which is not satisfied in many applications of interest; while
linear convergence results that do not require the strong convexity assumption
are usually asymptotic in nature. In this paper, we combine the best of these
two types of results and establish---under the standard assumption that the
gradient approximation errors decrease linearly to zero---the non-asymptotic
linear convergence of IGMs when applied to a class of structured convex
optimization problems. Such a class covers settings where the objective
function is not necessarily strongly convex and includes the least squares and
logistic regression problems. We believe that our techniques will find further
applications in the non-asymptotic convergence analysis of other first-order
methods.
| Anthony Man-Cho So | null | 1309.0113 | null | null |
API design for machine learning software: experiences from the
scikit-learn project | cs.LG cs.MS | Scikit-learn is an increasingly popular machine learning li- brary. Written
in Python, it is designed to be simple and efficient, accessible to
non-experts, and reusable in various contexts. In this paper, we present and
discuss our design choices for the application programming interface (API) of
the project. In particular, we describe the simple and elegant interface shared
by all learning and processing units in the library and then discuss its
advantages in terms of composition and reusability. The paper also comments on
implementation details specific to the Python ecosystem and analyzes obstacles
faced by users and developers of the library.
| Lars Buitinck (ILPS), Gilles Louppe, Mathieu Blondel, Fabian Pedregosa
(INRIA Saclay - Ile de France), Andreas Mueller, Olivier Grisel, Vlad
Niculae, Peter Prettenhofer, Alexandre Gramfort (INRIA Saclay - Ile de
France, LTCI), Jaques Grobler (INRIA Saclay - Ile de France), Robert Layton,
Jake Vanderplas, Arnaud Joly, Brian Holt, Ga\"el Varoquaux (INRIA Saclay -
Ile de France) | null | 1309.0238 | null | null |
Ensemble approaches for improving community detection methods | physics.soc-ph cs.LG cs.SI stat.ML | Statistical estimates can often be improved by fusion of data from several
different sources. One example is so-called ensemble methods which have been
successfully applied in areas such as machine learning for classification and
clustering. In this paper, we present an ensemble method to improve community
detection by aggregating the information found in an ensemble of community
structures. This ensemble can found by re-sampling methods, multiple runs of a
stochastic community detection method, or by several different community
detection algorithms applied to the same network. The proposed method is
evaluated using random networks with community structures and compared with two
commonly used community detection methods. The proposed method when applied on
a stochastic community detection algorithm performs well with low computational
complexity, thus offering both a new approach to community detection and an
additional community detection method.
| Johan Dahlin and Pontus Svenson | null | 1309.0242 | null | null |
Unmixing Incoherent Structures of Big Data by Randomized or Greedy
Decomposition | stat.ML cs.DS cs.LG | Learning big data by matrix decomposition always suffers from expensive
computation, mixing of complicated structures and noise. In this paper, we
study more adaptive models and efficient algorithms that decompose a data
matrix as the sum of semantic components with incoherent structures. We firstly
introduce "GO decomposition (GoDec)", an alternating projection method
estimating the low-rank part $L$ and the sparse part $S$ from data matrix
$X=L+S+G$ corrupted by noise $G$. Two acceleration strategies are proposed to
obtain scalable unmixing algorithm on big data: 1) Bilateral random projection
(BRP) is developed to speed up the update of $L$ in GoDec by a closed-form
built from left and right random projections of $X-S$ in lower dimensions; 2)
Greedy bilateral (GreB) paradigm updates the left and right factors of $L$ in a
mutually adaptive and greedy incremental manner, and achieve significant
improvement in both time and sample complexities. Then we proposes three
nontrivial variants of GoDec that generalizes GoDec to more general data type
and whose fast algorithms can be derived from the two strategies......
| Tianyi Zhou and Dacheng Tao | null | 1309.0302 | null | null |
Scalable Probabilistic Entity-Topic Modeling | stat.ML cs.IR cs.LG | We present an LDA approach to entity disambiguation. Each topic is associated
with a Wikipedia article and topics generate either content words or entity
mentions. Training such models is challenging because of the topic and
vocabulary size, both in the millions. We tackle these problems using a novel
distributed inference and representation framework based on a parallel Gibbs
sampler guided by the Wikipedia link graph, and pipelines of MapReduce allowing
fast and memory-frugal processing of large datasets. We report state-of-the-art
performance on a public dataset.
| Neil Houlsby, Massimiliano Ciaramita | null | 1309.0337 | null | null |
Relative Comparison Kernel Learning with Auxiliary Kernels | cs.LG | In this work we consider the problem of learning a positive semidefinite
kernel matrix from relative comparisons of the form: "object A is more similar
to object B than it is to C", where comparisons are given by humans. Existing
solutions to this problem assume many comparisons are provided to learn a high
quality kernel. However, this can be considered unrealistic for many real-world
tasks since relative assessments require human input, which is often costly or
difficult to obtain. Because of this, only a limited number of these
comparisons may be provided. In this work, we explore methods for aiding the
process of learning a kernel with the help of auxiliary kernels built from more
easily extractable information regarding the relationships among objects. We
propose a new kernel learning approach in which the target kernel is defined as
a conic combination of auxiliary kernels and a kernel whose elements are
learned directly. We formulate a convex optimization to solve for this target
kernel that adds only minor overhead to methods that use no auxiliary
information. Empirical results show that in the presence of few training
relative comparisons, our method can learn kernels that generalize to more
out-of-sample comparisons than methods that do not utilize auxiliary
information, as well as similar methods that learn metrics over objects.
| Eric Heim (University of Pittsburgh), Hamed Valizadegan (NASA Ames
Research Center), and Milos Hauskrecht (University of Pittsburgh) | null | 1309.0489 | null | null |
BayesOpt: A Library for Bayesian optimization with Robotics Applications | cs.RO cs.AI cs.LG cs.MS | The purpose of this paper is twofold. On one side, we present a general
framework for Bayesian optimization and we compare it with some related fields
in active learning and Bayesian numerical analysis. On the other hand, Bayesian
optimization and related problems (bandits, sequential experimental design) are
highly dependent on the surrogate model that is selected. However, there is no
clear standard in the literature. Thus, we present a fast and flexible toolbox
that allows to test and combine different models and criteria with little
effort. It includes most of the state-of-the-art contributions, algorithms and
models. Its speed also removes part of the stigma that Bayesian optimization
methods are only good for "expensive functions". The software is free and it
can be used in many operating systems and computer languages.
| Ruben Martinez-Cantin | null | 1309.0671 | null | null |
Online Tensor Methods for Learning Latent Variable Models | cs.LG cs.DC cs.SI stat.ML | We introduce an online tensor decomposition based approach for two latent
variable modeling problems namely, (1) community detection, in which we learn
the latent communities that the social actors in social networks belong to, and
(2) topic modeling, in which we infer hidden topics of text articles. We
consider decomposition of moment tensors using stochastic gradient descent. We
conduct optimization of multilinear operations in SGD and avoid directly
forming the tensors, to save computational and storage costs. We present
optimized algorithm in two platforms. Our GPU-based implementation exploits the
parallelism of SIMD architectures to allow for maximum speed-up by a careful
optimization of storage and data transfer, whereas our CPU-based implementation
uses efficient sparse matrix computations and is suitable for large sparse
datasets. For the community detection problem, we demonstrate accuracy and
computational efficiency on Facebook, Yelp and DBLP datasets, and for the topic
modeling problem, we also demonstrate good performance on the New York Times
dataset. We compare our results to the state-of-the-art algorithms such as the
variational method, and report a gain of accuracy and a gain of several orders
of magnitude in the execution time.
| Furong Huang, U. N. Niranjan, Mohammad Umar Hakeem, Animashree
Anandkumar | null | 1309.0787 | null | null |
SKYNET: an efficient and robust neural network training tool for machine
learning in astronomy | astro-ph.IM cs.LG cs.NE physics.data-an stat.ML | We present the first public release of our generic neural network training
algorithm, called SkyNet. This efficient and robust machine learning tool is
able to train large and deep feed-forward neural networks, including
autoencoders, for use in a wide range of supervised and unsupervised learning
applications, such as regression, classification, density estimation,
clustering and dimensionality reduction. SkyNet uses a `pre-training' method to
obtain a set of network parameters that has empirically been shown to be close
to a good solution, followed by further optimisation using a regularised
variant of Newton's method, where the level of regularisation is determined and
adjusted automatically; the latter uses second-order derivative information to
improve convergence, but without the need to evaluate or store the full Hessian
matrix, by using a fast approximate method to calculate Hessian-vector
products. This combination of methods allows for the training of complicated
networks that are difficult to optimise using standard backpropagation
techniques. SkyNet employs convergence criteria that naturally prevent
overfitting, and also includes a fast algorithm for estimating the accuracy of
network outputs. The utility and flexibility of SkyNet are demonstrated by
application to a number of toy problems, and to astronomical problems focusing
on the recovery of structure from blurred and noisy images, the identification
of gamma-ray bursters, and the compression and denoising of galaxy images. The
SkyNet software, which is implemented in standard ANSI C and fully parallelised
using MPI, is available at http://www.mrao.cam.ac.uk/software/skynet/.
| Philip Graff, Farhan Feroz, Michael P. Hobson, Anthony N. Lasenby | 10.1093/mnras/stu642 | 1309.0790 | null | null |
On the Robustness of Temporal Properties for Stochastic Models | cs.LO cs.AI cs.LG cs.SY | Stochastic models such as Continuous-Time Markov Chains (CTMC) and Stochastic
Hybrid Automata (SHA) are powerful formalisms to model and to reason about the
dynamics of biological systems, due to their ability to capture the
stochasticity inherent in biological processes. A classical question in formal
modelling with clear relevance to biological modelling is the model checking
problem. i.e. calculate the probability that a behaviour, expressed for
instance in terms of a certain temporal logic formula, may occur in a given
stochastic process. However, one may not only be interested in the notion of
satisfiability, but also in the capacity of a system to mantain a particular
emergent behaviour unaffected by the perturbations, caused e.g. from extrinsic
noise, or by possible small changes in the model parameters. To address this
issue, researchers from the verification community have recently proposed
several notions of robustness for temporal logic providing suitable definitions
of distance between a trajectory of a (deterministic) dynamical system and the
boundaries of the set of trajectories satisfying the property of interest. The
contributions of this paper are twofold. First, we extend the notion of
robustness to stochastic systems, showing that this naturally leads to a
distribution of robustness scores. By discussing two examples, we show how to
approximate the distribution of the robustness score and its key indicators:
the average robustness and the conditional average robustness. Secondly, we
show how to combine these indicators with the satisfaction probability to
address the system design problem, where the goal is to optimize some control
parameters of a stochastic model in order to best maximize robustness of the
desired specifications.
| Ezio Bartocci (TU Wien, Austria), Luca Bortolussi (University of
Trieste, Italy), Laura Nenzi (IMT Lucca, Italy), Guido Sanguinetti
(University of Edinburgh, UK) | 10.4204/EPTCS.125.1 | 1309.0866 | null | null |
Concentration in unbounded metric spaces and algorithmic stability | math.PR cs.LG math.FA | We prove an extension of McDiarmid's inequality for metric spaces with
unbounded diameter. To this end, we introduce the notion of the {\em
subgaussian diameter}, which is a distribution-dependent refinement of the
metric diameter. Our technique provides an alternative approach to that of
Kutin and Niyogi's method of weakly difference-bounded functions, and yields
nontrivial, dimension-free results in some interesting cases where the former
does not. As an application, we give apparently the first generalization bound
in the algorithmic stability setting that holds for unbounded loss functions.
We furthermore extend our concentration inequality to strongly mixing
processes.
| Aryeh Kontorovich | null | 1309.1007 | null | null |
Confidence-constrained joint sparsity recovery under the Poisson noise
model | stat.ML cs.LG | Our work is focused on the joint sparsity recovery problem where the common
sparsity pattern is corrupted by Poisson noise. We formulate the
confidence-constrained optimization problem in both least squares (LS) and
maximum likelihood (ML) frameworks and study the conditions for perfect
reconstruction of the original row sparsity and row sparsity pattern. However,
the confidence-constrained optimization problem is non-convex. Using convex
relaxation, an alternative convex reformulation of the problem is proposed. We
evaluate the performance of the proposed approach using simulation results on
synthetic data and show the effectiveness of proposed row sparsity and row
sparsity pattern recovery framework.
| E. Chunikhina, R. Raich, and T. Nguyen | null | 1309.1193 | null | null |
Semistochastic Quadratic Bound Methods | stat.ML cs.LG math.NA stat.CO | Partition functions arise in a variety of settings, including conditional
random fields, logistic regression, and latent gaussian models. In this paper,
we consider semistochastic quadratic bound (SQB) methods for maximum likelihood
inference based on partition function optimization. Batch methods based on the
quadratic bound were recently proposed for this class of problems, and
performed favorably in comparison to state-of-the-art techniques.
Semistochastic methods fall in between batch algorithms, which use all the
data, and stochastic gradient type methods, which use small random selections
at each iteration. We build semistochastic quadratic bound-based methods, and
prove both global convergence (to a stationary point) under very weak
assumptions, and linear convergence rate under stronger assumptions on the
objective. To make the proposed methods faster and more stable, we consider
inexact subproblem minimization and batch-size selection schemes. The efficacy
of SQB methods is demonstrated via comparison with several state-of-the-art
techniques on commonly used datasets.
| Aleksandr Y. Aravkin, Anna Choromanska, Tony Jebara, and Dimitri
Kanevsky | null | 1309.1369 | null | null |
Bayesian Structural Inference for Hidden Processes | stat.ML cs.LG math.ST nlin.CD physics.data-an stat.TH | We introduce a Bayesian approach to discovering patterns in structurally
complex processes. The proposed method of Bayesian Structural Inference (BSI)
relies on a set of candidate unifilar HMM (uHMM) topologies for inference of
process structure from a data series. We employ a recently developed exact
enumeration of topological epsilon-machines. (A sequel then removes the
topological restriction.) This subset of the uHMM topologies has the added
benefit that inferred models are guaranteed to be epsilon-machines,
irrespective of estimated transition probabilities. Properties of
epsilon-machines and uHMMs allow for the derivation of analytic expressions for
estimating transition probabilities, inferring start states, and comparing the
posterior probability of candidate model topologies, despite process internal
structure being only indirectly present in data. We demonstrate BSI's
effectiveness in estimating a process's randomness, as reflected by the Shannon
entropy rate, and its structure, as quantified by the statistical complexity.
We also compare using the posterior distribution over candidate models and the
single, maximum a posteriori model for point estimation and show that the
former more accurately reflects uncertainty in estimated values. We apply BSI
to in-class examples of finite- and infinite-order Markov processes, as well to
an out-of-class, infinite-state hidden process.
| Christopher C. Strelioff and James P. Crutchfield | 10.1103/PhysRevE.89.042119 | 1309.1392 | null | null |
Improvements to deep convolutional neural networks for LVCSR | cs.LG cs.CL cs.NE math.OC stat.ML | Deep Convolutional Neural Networks (CNNs) are more powerful than Deep Neural
Networks (DNN), as they are able to better reduce spectral variation in the
input signal. This has also been confirmed experimentally, with CNNs showing
improvements in word error rate (WER) between 4-12% relative compared to DNNs
across a variety of LVCSR tasks. In this paper, we describe different methods
to further improve CNN performance. First, we conduct a deep analysis comparing
limited weight sharing and full weight sharing with state-of-the-art features.
Second, we apply various pooling strategies that have shown improvements in
computer vision to an LVCSR speech task. Third, we introduce a method to
effectively incorporate speaker adaptation, namely fMLLR, into log-mel
features. Fourth, we introduce an effective strategy to use dropout during
Hessian-free sequence training. We find that with these improvements,
particularly with fMLLR and dropout, we are able to achieve an additional 2-3%
relative improvement in WER on a 50-hour Broadcast News task over our previous
best CNN baseline. On a larger 400-hour BN task, we find an additional 4-5%
relative improvement over our previous best CNN baseline.
| Tara N. Sainath, Brian Kingsbury, Abdel-rahman Mohamed, George E.
Dahl, George Saon, Hagen Soltau, Tomas Beran, Aleksandr Y. Aravkin, Bhuvana
Ramabhadran | null | 1309.1501 | null | null |
Accelerating Hessian-free optimization for deep neural networks by
implicit preconditioning and sampling | cs.LG cs.CL cs.NE math.OC stat.ML | Hessian-free training has become a popular parallel second or- der
optimization technique for Deep Neural Network training. This study aims at
speeding up Hessian-free training, both by means of decreasing the amount of
data used for training, as well as through reduction of the number of Krylov
subspace solver iterations used for implicit estimation of the Hessian. In this
paper, we develop an L-BFGS based preconditioning scheme that avoids the need
to access the Hessian explicitly. Since L-BFGS cannot be regarded as a
fixed-point iteration, we further propose the employment of flexible Krylov
subspace solvers that retain the desired theoretical convergence guarantees of
their conventional counterparts. Second, we propose a new sampling algorithm,
which geometrically increases the amount of data utilized for gradient and
Krylov subspace iteration calculations. On a 50-hr English Broadcast News task,
we find that these methodologies provide roughly a 1.5x speed-up, whereas, on a
300-hr Switchboard task, these techniques provide over a 2.3x speedup, with no
loss in WER. These results suggest that even further speed-up is expected, as
problems scale and complexity grows.
| Tara N. Sainath, Lior Horesh, Brian Kingsbury, Aleksandr Y. Aravkin,
Bhuvana Ramabhadran | null | 1309.1508 | null | null |
Projection onto the probability simplex: An efficient algorithm with a
simple proof, and an application | cs.LG math.OC stat.ML | We provide an elementary proof of a simple, efficient algorithm for computing
the Euclidean projection of a point onto the probability simplex. We also show
an application in Laplacian K-modes clustering.
| Weiran Wang, Miguel \'A. Carreira-Perpi\~n\'an | null | 1309.1541 | null | null |
A Comparism of the Performance of Supervised and Unsupervised Machine
Learning Techniques in evolving Awale/Mancala/Ayo Game Player | cs.LG cs.GT | Awale games have become widely recognized across the world, for their
innovative strategies and techniques which were used in evolving the agents
(player) and have produced interesting results under various conditions. This
paper will compare the results of the two major machine learning techniques by
reviewing their performance when using minimax, endgame database, a combination
of both techniques or other techniques, and will determine which are the best
techniques.
| O.A. Randle, O. O. Ogunduyile, T. Zuva, N. A. Fashola | null | 1309.1543 | null | null |
Convergence of Nearest Neighbor Pattern Classification with Selective
Sampling | cs.LG stat.ML | In the panoply of pattern classification techniques, few enjoy the intuitive
appeal and simplicity of the nearest neighbor rule: given a set of samples in
some metric domain space whose value under some function is known, we estimate
the function anywhere in the domain by giving the value of the nearest sample
per the metric. More generally, one may use the modal value of the m nearest
samples, where m is a fixed positive integer (although m=1 is known to be
admissible in the sense that no larger value is asymptotically superior in
terms of prediction error). The nearest neighbor rule is nonparametric and
extremely general, requiring in principle only that the domain be a metric
space. The classic paper on the technique, proving convergence under
independent, identically-distributed (iid) sampling, is due to Cover and Hart
(1967). Because taking samples is costly, there has been much research in
recent years on selective sampling, in which each sample is selected from a
pool of candidates ranked by a heuristic; the heuristic tries to guess which
candidate would be the most "informative" sample. Lindenbaum et al. (2004)
apply selective sampling to the nearest neighbor rule, but their approach
sacrifices the austere generality of Cover and Hart; furthermore, their
heuristic algorithm is complex and computationally expensive. Here we report
recent results that enable selective sampling in the original Cover-Hart
setting. Our results pose three selection heuristics and prove that their
nearest neighbor rule predictions converge to the true pattern. Two of the
algorithms are computationally cheap, with complexity growing linearly in the
number of samples. We believe that these results constitute an important
advance in the art.
| Shaun N. Joseph and Seif Omar Abu Bakr and Gabriel Lugo | null | 1309.1761 | null | null |
A General Two-Step Approach to Learning-Based Hashing | cs.LG cs.CV | Most existing approaches to hashing apply a single form of hash function, and
an optimization process which is typically deeply coupled to this specific
form. This tight coupling restricts the flexibility of the method to respond to
the data, and can result in complex optimization problems that are difficult to
solve. Here we propose a flexible yet simple framework that is able to
accommodate different types of loss functions and hash functions. This
framework allows a number of existing approaches to hashing to be placed in
context, and simplifies the development of new problem-specific hashing
methods. Our framework decomposes hashing learning problem into two steps: hash
bit learning and hash function learning based on the learned bits. The first
step can typically be formulated as binary quadratic problems, and the second
step can be accomplished by training standard binary classifiers. Both problems
have been extensively studied in the literature. Our extensive experiments
demonstrate that the proposed framework is effective, flexible and outperforms
the state-of-the-art.
| Guosheng Lin, Chunhua Shen, David Suter, Anton van den Hengel | null | 1309.1853 | null | null |
A Clustering Approach to Learn Sparsely-Used Overcomplete Dictionaries | stat.ML cs.LG math.OC | We consider the problem of learning overcomplete dictionaries in the context
of sparse coding, where each sample selects a sparse subset of dictionary
elements. Our main result is a strategy to approximately recover the unknown
dictionary using an efficient algorithm. Our algorithm is a clustering-style
procedure, where each cluster is used to estimate a dictionary element. The
resulting solution can often be further cleaned up to obtain a high accuracy
estimate, and we provide one simple scenario where $\ell_1$-regularized
regression can be used for such a second stage.
| Alekh Agarwal and Animashree Anandkumar and Praneeth Netrapalli | null | 1309.1952 | null | null |
Learning Transformations for Clustering and Classification | cs.CV cs.LG stat.ML | A low-rank transformation learning framework for subspace clustering and
classification is here proposed. Many high-dimensional data, such as face
images and motion sequences, approximately lie in a union of low-dimensional
subspaces. The corresponding subspace clustering problem has been extensively
studied in the literature to partition such high-dimensional data into clusters
corresponding to their underlying low-dimensional subspaces. However,
low-dimensional intrinsic structures are often violated for real-world
observations, as they can be corrupted by errors or deviate from ideal models.
We propose to address this by learning a linear transformation on subspaces
using matrix rank, via its convex surrogate nuclear norm, as the optimization
criteria. The learned linear transformation restores a low-rank structure for
data from the same subspace, and, at the same time, forces a a maximally
separated structure for data from different subspaces. In this way, we reduce
variations within subspaces, and increase separation between subspaces for a
more robust subspace clustering. This proposed learned robust subspace
clustering framework significantly enhances the performance of existing
subspace clustering methods. Basic theoretical results here presented help to
further support the underlying framework. To exploit the low-rank structures of
the transformed subspaces, we further introduce a fast subspace clustering
technique, which efficiently combines robust PCA with sparse modeling. When
class labels are present at the training stage, we show this low-rank
transformation framework also significantly enhances classification
performance. Extensive experiments using public datasets are presented, showing
that the proposed approach significantly outperforms state-of-the-art methods
for subspace clustering and classification.
| Qiang Qiu, Guillermo Sapiro | null | 1309.2074 | null | null |
Structure Learning of Probabilistic Logic Programs by Searching the
Clause Space | cs.LG cs.AI | Learning probabilistic logic programming languages is receiving an increasing
attention and systems are available for learning the parameters (PRISM,
LeProbLog, LFI-ProbLog and EMBLEM) or both the structure and the parameters
(SEM-CP-logic and SLIPCASE) of these languages. In this paper we present the
algorithm SLIPCOVER for "Structure LearnIng of Probabilistic logic programs by
searChing OVER the clause space". It performs a beam search in the space of
probabilistic clauses and a greedy search in the space of theories, using the
log likelihood of the data as the guiding heuristics. To estimate the log
likelihood SLIPCOVER performs Expectation Maximization with EMBLEM. The
algorithm has been tested on five real world datasets and compared with
SLIPCASE, SEM-CP-logic, Aleph and two algorithms for learning Markov Logic
Networks (Learning using Structural Motifs (LSM) and ALEPH++ExactL1). SLIPCOVER
achieves higher areas under the precision-recall and ROC curves in most cases.
| Elena Bellodi, Fabrizio Riguzzi | 10.1017/S1471068413000689 | 1309.2080 | null | null |
Large-scale optimization with the primal-dual column generation method | math.OC cs.LG cs.NA | The primal-dual column generation method (PDCGM) is a general-purpose column
generation technique that relies on the primal-dual interior point method to
solve the restricted master problems. The use of this interior point method
variant allows to obtain suboptimal and well-centered dual solutions which
naturally stabilizes the column generation. As recently presented in the
literature, reductions in the number of calls to the oracle and in the CPU
times are typically observed when compared to the standard column generation,
which relies on extreme optimal dual solutions. However, these results are
based on relatively small problems obtained from linear relaxations of
combinatorial applications. In this paper, we investigate the behaviour of the
PDCGM in a broader context, namely when solving large-scale convex optimization
problems. We have selected applications that arise in important real-life
contexts such as data analysis (multiple kernel learning problem),
decision-making under uncertainty (two-stage stochastic programming problems)
and telecommunication and transportation networks (multicommodity network flow
problem). In the numerical experiments, we use publicly available benchmark
instances to compare the performance of the PDCGM against recent results for
different methods presented in the literature, which were the best available
results to date. The analysis of these results suggests that the PDCGM offers
an attractive alternative over specialized methods since it remains competitive
in terms of number of iterations and CPU times even for large-scale
optimization problems.
| Jacek Gondzio, Pablo Gonz\'alez-Brevis and Pedro Munari | null | 1309.2168 | null | null |
Exponentially Fast Parameter Estimation in Networks Using Distributed
Dual Averaging | cs.LG cs.SI math.OC stat.ML | In this paper we present an optimization-based view of distributed parameter
estimation and observational social learning in networks. Agents receive a
sequence of random, independent and identically distributed (i.i.d.) signals,
each of which individually may not be informative about the underlying true
state, but the signals together are globally informative enough to make the
true state identifiable. Using an optimization-based characterization of
Bayesian learning as proximal stochastic gradient descent (with
Kullback-Leibler divergence from a prior as a proximal function), we show how
to efficiently use a distributed, online variant of Nesterov's dual averaging
method to solve the estimation with purely local information. When the true
state is globally identifiable, and the network is connected, we prove that
agents eventually learn the true parameter using a randomized gossip scheme. We
demonstrate that with high probability the convergence is exponentially fast
with a rate dependent on the KL divergence of observations under the true state
from observations under the second likeliest state. Furthermore, our work also
highlights the possibility of learning under continuous adaptation of network
which is a consequence of employing constant, unit stepsize for the algorithm.
| Shahin Shahrampour and Ali Jadbabaie | null | 1309.2350 | null | null |
Accelerated Proximal Stochastic Dual Coordinate Ascent for Regularized
Loss Minimization | stat.ML cs.LG cs.NA stat.CO | We introduce a proximal version of the stochastic dual coordinate ascent
method and show how to accelerate the method using an inner-outer iteration
procedure. We analyze the runtime of the framework and obtain rates that
improve state-of-the-art results for various key machine learning optimization
problems including SVM, logistic regression, ridge regression, Lasso, and
multiclass SVM. Experiments validate our theoretical findings.
| Shai Shalev-Shwartz and Tong Zhang | null | 1309.2375 | null | null |
Minimizing Finite Sums with the Stochastic Average Gradient | math.OC cs.LG stat.CO stat.ML | We propose the stochastic average gradient (SAG) method for optimizing the
sum of a finite number of smooth convex functions. Like stochastic gradient
(SG) methods, the SAG method's iteration cost is independent of the number of
terms in the sum. However, by incorporating a memory of previous gradient
values the SAG method achieves a faster convergence rate than black-box SG
methods. The convergence rate is improved from O(1/k^{1/2}) to O(1/k) in
general, and when the sum is strongly-convex the convergence rate is improved
from the sub-linear O(1/k) to a linear convergence rate of the form O(p^k) for
p \textless{} 1. Further, in many cases the convergence rate of the new method
is also faster than black-box deterministic gradient methods, in terms of the
number of gradient evaluations. Numerical experiments indicate that the new
algorithm often dramatically outperforms existing SG and deterministic gradient
methods, and that the performance may be further improved through the use of
non-uniform sampling strategies.
| Mark Schmidt (SIERRA, LIENS), Nicolas Le Roux (SIERRA, LIENS), Francis
Bach (SIERRA, LIENS) | null | 1309.2388 | null | null |
Maximizing submodular functions using probabilistic graphical models | cs.LG math.OC | We consider the problem of maximizing submodular functions; while this
problem is known to be NP-hard, several numerically efficient local search
techniques with approximation guarantees are available. In this paper, we
propose a novel convex relaxation which is based on the relationship between
submodular functions, entropies and probabilistic graphical models. In a
graphical model, the entropy of the joint distribution decomposes as a sum of
marginal entropies of subsets of variables; moreover, for any distribution, the
entropy of the closest distribution factorizing in the graphical model provides
an bound on the entropy. For directed graphical models, this last property
turns out to be a direct consequence of the submodularity of the entropy
function, and allows the generalization of graphical-model-based upper bounds
to any submodular functions. These upper bounds may then be jointly maximized
with respect to a set, while minimized with respect to the graph, leading to a
convex variational inference scheme for maximizing submodular functions, based
on outer approximations of the marginal polytope and maximum likelihood bounded
treewidth structures. By considering graphs of increasing treewidths, we may
then explore the trade-off between computational complexity and tightness of
the relaxation. We also present extensions to constrained problems and
maximizing the difference of submodular functions, which include all possible
set functions.
| K. S. Sesh Kumar (LIENS, INRIA Paris - Rocquencourt), Francis Bach
(LIENS, INRIA Paris - Rocquencourt) | null | 1309.2593 | null | null |
Enhancements of Multi-class Support Vector Machine Construction from
Binary Learners using Generalization Performance | cs.LG stat.ML | We propose several novel methods for enhancing the multi-class SVMs by
applying the generalization performance of binary classifiers as the core idea.
This concept will be applied on the existing algorithms, i.e., the Decision
Directed Acyclic Graph (DDAG), the Adaptive Directed Acyclic Graphs (ADAG), and
Max Wins. Although in the previous approaches there have been many attempts to
use some information such as the margin size and the number of support vectors
as performance estimators for binary SVMs, they may not accurately reflect the
actual performance of the binary SVMs. We show that the generalization ability
evaluated via a cross-validation mechanism is more suitable to directly extract
the actual performance of binary SVMs. Our methods are built around this
performance measure, and each of them is crafted to overcome the weakness of
the previous algorithm. The proposed methods include the Reordering Adaptive
Directed Acyclic Graph (RADAG), Strong Elimination of the classifiers (SE),
Weak Elimination of the classifiers (WE), and Voting based Candidate Filtering
(VCF). Experimental results demonstrate that our methods give significantly
higher accuracy than all of the traditional ones. Especially, WE provides
significantly superior results compared to Max Wins which is recognized as the
state of the art algorithm in terms of both accuracy and classification speed
with two times faster in average.
| Patoomsiri Songsiri, Thimaporn Phetkaew, Boonserm Kijsirikul | null | 1309.2765 | null | null |
Decision Trees for Function Evaluation - Simultaneous Optimization of
Worst and Expected Cost | cs.DS cs.AI cs.LG | In several applications of automatic diagnosis and active learning a central
problem is the evaluation of a discrete function by adaptively querying the
values of its variables until the values read uniquely determine the value of
the function. In general, the process of reading the value of a variable might
involve some cost, computational or even a fee to be paid for the experiment
required for obtaining the value. This cost should be taken into account when
deciding the next variable to read. The goal is to design a strategy for
evaluating the function incurring little cost (in the worst case or in
expectation according to a prior distribution on the possible variables'
assignments). Our algorithm builds a strategy (decision tree) which attains a
logarithmic approxima- tion simultaneously for the expected and worst cost
spent. This is best possible under the assumption that $P \neq NP.$
| Ferdinando Cicalese and Eduardo Laber and Aline Medeiros Saettler | null | 1309.2796 | null | null |
High-dimensional cluster analysis with the Masked EM Algorithm | q-bio.QM cs.LG q-bio.NC stat.AP | Cluster analysis faces two problems in high dimensions: first, the `curse of
dimensionality' that can lead to overfitting and poor generalization
performance; and second, the sheer time taken for conventional algorithms to
process large amounts of high-dimensional data. In many applications, only a
small subset of features provide information about the cluster membership of
any one data point, however this informative feature subset may not be the same
for all data points. Here we introduce a `Masked EM' algorithm for fitting
mixture of Gaussians models in such cases. We show that the algorithm performs
close to optimally on simulated Gaussian data, and in an application of `spike
sorting' of high channel-count neuronal recordings.
| Shabnam N. Kadir, Dan F. M. Goodman, and Kenneth D. Harris | null | 1309.2848 | null | null |
Temporal Autoencoding Improves Generative Models of Time Series | stat.ML cs.LG | Restricted Boltzmann Machines (RBMs) are generative models which can learn
useful representations from samples of a dataset in an unsupervised fashion.
They have been widely employed as an unsupervised pre-training method in
machine learning. RBMs have been modified to model time series in two main
ways: The Temporal RBM stacks a number of RBMs laterally and introduces
temporal dependencies between the hidden layer units; The Conditional RBM, on
the other hand, considers past samples of the dataset as a conditional bias and
learns a representation which takes these into account. Here we propose a new
training method for both the TRBM and the CRBM, which enforces the dynamic
structure of temporal datasets. We do so by treating the temporal models as
denoising autoencoders, considering past frames of the dataset as corrupted
versions of the present frame and minimizing the reconstruction error of the
present data by the model. We call this approach Temporal Autoencoding. This
leads to a significant improvement in the performance of both models in a
filling-in-frames task across a number of datasets. The error reduction for
motion capture data is 56\% for the CRBM and 80\% for the TRBM. Taking the
posterior mean prediction instead of single samples further improves the
model's estimates, decreasing the error by as much as 91\% for the CRBM on
motion capture data. We also trained the model to perform forecasting on a
large number of datasets and have found TA pretraining to consistently improve
the performance of the forecasts. Furthermore, by looking at the prediction
error across time, we can see that this improvement reflects a better
representation of the dynamics of the data as opposed to a bias towards
reconstructing the observed data on a short time scale.
| Chris H\"ausler, Alex Susemihl, Martin P Nawrot, Manfred Opper | null | 1309.3103 | null | null |
Convex relaxations of structured matrix factorizations | cs.LG math.OC | We consider the factorization of a rectangular matrix $X $ into a positive
linear combination of rank-one factors of the form $u v^\top$, where $u$ and
$v$ belongs to certain sets $\mathcal{U}$ and $\mathcal{V}$, that may encode
specific structures regarding the factors, such as positivity or sparsity. In
this paper, we show that computing the optimal decomposition is equivalent to
computing a certain gauge function of $X$ and we provide a detailed analysis of
these gauge functions and their polars. Since these gauge functions are
typically hard to compute, we present semi-definite relaxations and several
algorithms that may recover approximate decompositions with approximation
guarantees. We illustrate our results with simulations on finding
decompositions with elements in $\{0,1\}$. As side contributions, we present a
detailed analysis of variational quadratic representations of norms as well as
a new iterative basis pursuit algorithm that can deal with inexact first-order
oracles.
| Francis Bach (INRIA Paris - Rocquencourt, LIENS) | null | 1309.3117 | null | null |
Efficient Orthogonal Tensor Decomposition, with an Application to Latent
Variable Model Learning | stat.ML cs.LG math.ST stat.TH | Decomposing tensors into orthogonal factors is a well-known task in
statistics, machine learning, and signal processing. We study orthogonal outer
product decompositions where the factors in the summands in the decomposition
are required to be orthogonal across summands, by relating this orthogonal
decomposition to the singular value decompositions of the flattenings. We show
that it is a non-trivial assumption for a tensor to have such an orthogonal
decomposition, and we show that it is unique (up to natural symmetries) in case
it exists, in which case we also demonstrate how it can be efficiently and
reliably obtained by a sequence of singular value decompositions. We
demonstrate how the factoring algorithm can be applied for parameter
identification in latent variable and mixture models.
| Franz J. Kir\'aly | null | 1309.3233 | null | null |
Recovery guarantees for exemplar-based clustering | stat.ML cs.CV cs.LG | For a certain class of distributions, we prove that the linear programming
relaxation of $k$-medoids clustering---a variant of $k$-means clustering where
means are replaced by exemplars from within the dataset---distinguishes points
drawn from nonoverlapping balls with high probability once the number of points
drawn and the separation distance between any two balls are sufficiently large.
Our results hold in the nontrivial regime where the separation distance is
small enough that points drawn from different balls may be closer to each other
than points drawn from the same ball; in this case, clustering by thresholding
pairwise distances between points can fail. We also exhibit numerical evidence
of high-probability recovery in a substantially more permissive regime.
| Abhinav Nellore and Rachel Ward | null | 1309.3256 | null | null |
Mixed Membership Models for Time Series | stat.ME cs.LG stat.ML | In this article we discuss some of the consequences of the mixed membership
perspective on time series analysis. In its most abstract form, a mixed
membership model aims to associate an individual entity with some set of
attributes based on a collection of observed data. Although much of the
literature on mixed membership models considers the setting in which
exchangeable collections of data are associated with each member of a set of
entities, it is equally natural to consider problems in which an entire time
series is viewed as an entity and the goal is to characterize the time series
in terms of a set of underlying dynamic attributes or "dynamic regimes".
Indeed, this perspective is already present in the classical hidden Markov
model, where the dynamic regimes are referred to as "states", and the
collection of states realized in a sample path of the underlying process can be
viewed as a mixed membership characterization of the observed time series. Our
goal here is to review some of the richer modeling possibilities for time
series that are provided by recent developments in the mixed membership
framework.
| Emily B. Fox and Michael I. Jordan | null | 1309.3533 | null | null |
Optimized projections for compressed sensing via rank-constrained
nearest correlation matrix | cs.IT cs.LG math.IT stat.ML | Optimizing the acquisition matrix is useful for compressed sensing of signals
that are sparse in overcomplete dictionaries, because the acquisition matrix
can be adapted to the particular correlations of the dictionary atoms. In this
paper a novel formulation of the optimization problem is proposed, in the form
of a rank-constrained nearest correlation matrix problem. Furthermore,
improvements for three existing optimization algorithms are introduced, which
are shown to be particular instances of the proposed formulation. Simulation
results show notable improvements and superior robustness in sparse signal
recovery.
| Nicolae Cleju | 10.1016/j.acha.2013.08.005 | 1309.3676 | null | null |
Group Learning and Opinion Diffusion in a Broadcast Network | cs.LG | We analyze the following group learning problem in the context of opinion
diffusion: Consider a network with $M$ users, each facing $N$ options. In a
discrete time setting, at each time step, each user chooses $K$ out of the $N$
options, and receive randomly generated rewards, whose statistics depend on the
options chosen as well as the user itself, and are unknown to the users. Each
user aims to maximize their expected total rewards over a certain time horizon
through an online learning process, i.e., a sequence of exploration (sampling
the return of each option) and exploitation (selecting empirically good
options) steps.
Within this context we consider two group learning scenarios, (1) users with
uniform preferences and (2) users with diverse preferences, and examine how a
user should construct its learning process to best extract information from
other's decisions and experiences so as to maximize its own reward. Performance
is measured in {\em weak regret}, the difference between the user's total
reward and the reward from a user-specific best single-action policy (i.e.,
always selecting the set of options generating the highest mean rewards for
this user). Within each scenario we also consider two cases: (i) when users
exchange full information, meaning they share the actual rewards they obtained
from their choices, and (ii) when users exchange limited information, e.g.,
only their choices but not rewards obtained from these choices.
| Yang Liu, Mingyan Liu | null | 1309.3697 | null | null |
Local Support Vector Machines:Formulation and Analysis | stat.ML cs.AI cs.LG | We provide a formulation for Local Support Vector Machines (LSVMs) that
generalizes previous formulations, and brings out the explicit connections to
local polynomial learning used in nonparametric estimation literature. We
investigate the simplest type of LSVMs called Local Linear Support Vector
Machines (LLSVMs). For the first time we establish conditions under which
LLSVMs make Bayes consistent predictions at each test point $x_0$. We also
establish rates at which the local risk of LLSVMs converges to the minimum
value of expected local risk at each point $x_0$. Using stability arguments we
establish generalization error bounds for LLSVMs.
| Ravi Ganti and Alexander Gray | null | 1309.3699 | null | null |
Visual-Semantic Scene Understanding by Sharing Labels in a Context
Network | cs.CV cs.LG stat.ML | We consider the problem of naming objects in complex, natural scenes
containing widely varying object appearance and subtly different names.
Informed by cognitive research, we propose an approach based on sharing context
based object hypotheses between visual and lexical spaces. To this end, we
present the Visual Semantic Integration Model (VSIM) that represents object
labels as entities shared between semantic and visual contexts and infers a new
image by updating labels through context switching. At the core of VSIM is a
semantic Pachinko Allocation Model and a visual nearest neighbor Latent
Dirichlet Allocation Model. For inference, we derive an iterative Data
Augmentation algorithm that pools the label probabilities and maximizes the
joint label posterior of an image. Our model surpasses the performance of
state-of-art methods in several visual tasks on the challenging SUN09 dataset.
| Ishani Chakraborty and Ahmed Elgammal | null | 1309.3809 | null | null |
A Metric-learning based framework for Support Vector Machines and
Multiple Kernel Learning | cs.LG | Most metric learning algorithms, as well as Fisher's Discriminant Analysis
(FDA), optimize some cost function of different measures of within-and
between-class distances. On the other hand, Support Vector Machines(SVMs) and
several Multiple Kernel Learning (MKL) algorithms are based on the SVM large
margin theory. Recently, SVMs have been analyzed from SVM and metric learning,
and to develop new algorithms that build on the strengths of each. Inspired by
the metric learning interpretation of SVM, we develop here a new
metric-learning based SVM framework in which we incorporate metric learning
concepts within SVM. We extend the optimization problem of SVM to include some
measure of the within-class distance and along the way we develop a new
within-class distance measure which is appropriate for SVM. In addition, we
adopt the same approach for MKL and show that it can be also formulated as a
Mahalanobis metric learning problem. Our end result is a number of SVM/MKL
algorithms that incorporate metric learning concepts. We experiment with them
on a set of benchmark datasets and observe important predictive performance
improvements.
| Huyen Do and Alexandros Kalousis | null | 1309.3877 | null | null |
Performance Investigation of Feature Selection Methods | cs.IR cs.CL cs.LG | Sentiment analysis or opinion mining has become an open research domain after
proliferation of Internet and Web 2.0 social media. People express their
attitudes and opinions on social media including blogs, discussion forums,
tweets, etc. and, sentiment analysis concerns about detecting and extracting
sentiment or opinion from online text. Sentiment based text classification is
different from topical text classification since it involves discrimination
based on expressed opinion on a topic. Feature selection is significant for
sentiment analysis as the opinionated text may have high dimensions, which can
adversely affect the performance of sentiment analysis classifier. This paper
explores applicability of feature selection methods for sentiment analysis and
investigates their performance for classification in term of recall, precision
and accuracy. Five feature selection methods (Document Frequency, Information
Gain, Gain Ratio, Chi Squared, and Relief-F) and three popular sentiment
feature lexicons (HM, GI and Opinion Lexicon) are investigated on movie reviews
corpus with a size of 2000 documents. The experimental results show that
Information Gain gave consistent results and Gain Ratio performs overall best
for sentimental feature selection while sentiment lexicons gave poor
performance. Furthermore, we found that performance of the classifier depends
on appropriate number of representative feature selected from text.
| Anuj sharma, Shubhamoy Dey | null | 1309.3949 | null | null |
The Cyborg Astrobiologist: Matching of Prior Textures by Image
Compression for Geological Mapping and Novelty Detection | cs.CV astro-ph.EP astro-ph.IM cs.LG | (abridged) We describe an image-comparison technique of Heidemann and Ritter
that uses image compression, and is capable of: (i) detecting novel textures in
a series of images, as well as of: (ii) alerting the user to the similarity of
a new image to a previously-observed texture. This image-comparison technique
has been implemented and tested using our Astrobiology Phone-cam system, which
employs Bluetooth communication to send images to a local laptop server in the
field for the image-compression analysis. We tested the system in a field site
displaying a heterogeneous suite of sandstones, limestones, mudstones and
coalbeds. Some of the rocks are partly covered with lichen. The image-matching
procedure of this system performed very well with data obtained through our
field test, grouping all images of yellow lichens together and grouping all
images of a coal bed together, and giving a 91% accuracy for similarity
detection. Such similarity detection could be employed to make maps of
different geological units. The novelty-detection performance of our system was
also rather good (a 64% accuracy). Such novelty detection may become valuable
in searching for new geological units, which could be of astrobiological
interest. The image-comparison technique is an unsupervised technique that is
not capable of directly classifying an image as containing a particular
geological feature; labeling of such geological features is done post facto by
human geologists associated with this study, for the purpose of analyzing the
system's performance. By providing more advanced capabilities for similarity
detection and novelty detection, this image-compression technique could be
useful in giving more scientific autonomy to robotic planetary rovers, and in
assisting human astronauts in their geological exploration and assessment.
| P.C. McGuire, A. Bonnici, K.R. Bruner, C. Gross, J. Orm\"o, R.A.
Smosna, S. Walter, L. Wendt | 10.1017/S1473550413000372 | 1309.4024 | null | null |
Domain and Function: A Dual-Space Model of Semantic Relations and
Compositions | cs.CL cs.AI cs.LG | Given appropriate representations of the semantic relations between carpenter
and wood and between mason and stone (for example, vectors in a vector space
model), a suitable algorithm should be able to recognize that these relations
are highly similar (carpenter is to wood as mason is to stone; the relations
are analogous). Likewise, with representations of dog, house, and kennel, an
algorithm should be able to recognize that the semantic composition of dog and
house, dog house, is highly similar to kennel (dog house and kennel are
synonymous). It seems that these two tasks, recognizing relations and
compositions, are closely connected. However, up to now, the best models for
relations are significantly different from the best models for compositions. In
this paper, we introduce a dual-space model that unifies these two tasks. This
model matches the performance of the best previous models for relations and
compositions. The dual-space model consists of a space for measuring domain
similarity and a space for measuring function similarity. Carpenter and wood
share the same domain, the domain of carpentry. Mason and stone share the same
domain, the domain of masonry. Carpenter and mason share the same function, the
function of artisans. Wood and stone share the same function, the function of
materials. In the composition dog house, kennel has some domain overlap with
both dog and house (the domains of pets and buildings). The function of kennel
is similar to the function of house (the function of shelters). By combining
domain and function similarities in various ways, we can model relations,
compositions, and other aspects of semantics.
| Peter D. Turney | 10.1613/jair.3640 | 1309.4035 | null | null |
Learning a Loopy Model For Semantic Segmentation Exactly | cs.LG cs.CV | Learning structured models using maximum margin techniques has become an
indispensable tool for com- puter vision researchers, as many computer vision
applications can be cast naturally as an image labeling problem. Pixel-based or
superpixel-based conditional random fields are particularly popular examples.
Typ- ically, neighborhood graphs, which contain a large number of cycles, are
used. As exact inference in loopy graphs is NP-hard in general, learning these
models without approximations is usually deemed infeasible. In this work we
show that, despite the theoretical hardness, it is possible to learn loopy
models exactly in practical applications. To this end, we analyze the use of
multiple approximate inference techniques together with cutting plane training
of structural SVMs. We show that our proposed method yields exact solutions
with an optimality guarantees in a computer vision application, for little
additional computational cost. We also propose a dynamic caching scheme to
accelerate training further, yielding runtimes that are comparable with
approximate methods. We hope that this insight can lead to a reconsideration of
the tractability of loopy models in computer vision.
| Andreas Christian Mueller, Sven Behnke | null | 1309.4061 | null | null |
Regularized Spectral Clustering under the Degree-Corrected Stochastic
Blockmodel | stat.ML cs.LG math.ST stat.TH | Spectral clustering is a fast and popular algorithm for finding clusters in
networks. Recently, Chaudhuri et al. (2012) and Amini et al.(2012) proposed
inspired variations on the algorithm that artificially inflate the node degrees
for improved statistical performance. The current paper extends the previous
statistical estimation results to the more canonical spectral clustering
algorithm in a way that removes any assumption on the minimum degree and
provides guidance on the choice of the tuning parameter. Moreover, our results
show how the "star shape" in the eigenvectors--a common feature of empirical
networks--can be explained by the Degree-Corrected Stochastic Blockmodel and
the Extended Planted Partition model, two statistical models that allow for
highly heterogeneous degrees. Throughout, the paper characterizes and justifies
several of the variations of the spectral clustering algorithm in terms of
these models.
| Tai Qin, Karl Rohe | null | 1309.4111 | null | null |
Attribute-Efficient Evolvability of Linear Functions | cs.LG q-bio.PE | In a seminal paper, Valiant (2006) introduced a computational model for
evolution to address the question of complexity that can arise through
Darwinian mechanisms. Valiant views evolution as a restricted form of
computational learning, where the goal is to evolve a hypothesis that is close
to the ideal function. Feldman (2008) showed that (correlational) statistical
query learning algorithms could be framed as evolutionary mechanisms in
Valiant's model. P. Valiant (2012) considered evolvability of real-valued
functions and also showed that weak-optimization algorithms that use
weak-evaluation oracles could be converted to evolutionary mechanisms.
In this work, we focus on the complexity of representations of evolutionary
mechanisms. In general, the reductions of Feldman and P. Valiant may result in
intermediate representations that are arbitrarily complex (polynomial-sized
circuits). We argue that biological constraints often dictate that the
representations have low complexity, such as constant depth and fan-in
circuits. We give mechanisms for evolving sparse linear functions under a large
class of smooth distributions. These evolutionary algorithms are
attribute-efficient in the sense that the size of the representations and the
number of generations required depend only on the sparsity of the target
function and the accuracy parameter, but have no dependence on the total number
of attributes.
| Elaine Angelino, Varun Kanade | null | 1309.4132 | null | null |
Temporal-Difference Learning to Assist Human Decision Making during the
Control of an Artificial Limb | cs.AI cs.LG cs.RO | In this work we explore the use of reinforcement learning (RL) to help with
human decision making, combining state-of-the-art RL algorithms with an
application to prosthetics. Managing human-machine interaction is a problem of
considerable scope, and the simplification of human-robot interfaces is
especially important in the domains of biomedical technology and rehabilitation
medicine. For example, amputees who control artificial limbs are often required
to quickly switch between a number of control actions or modes of operation in
order to operate their devices. We suggest that by learning to anticipate
(predict) a user's behaviour, artificial limbs could take on an active role in
a human's control decisions so as to reduce the burden on their users.
Recently, we showed that RL in the form of general value functions (GVFs) could
be used to accurately detect a user's control intent prior to their explicit
control choices. In the present work, we explore the use of temporal-difference
learning and GVFs to predict when users will switch their control influence
between the different motor functions of a robot arm. Experiments were
performed using a multi-function robot arm that was controlled by muscle
signals from a user's body (similar to conventional artificial limb control).
Our approach was able to acquire and maintain forecasts about a user's
switching decisions in real time. It also provides an intuitive and reward-free
way for users to correct or reinforce the decisions made by the machine
learning system. We expect that when a system is certain enough about its
predictions, it can begin to take over switching decisions from the user to
streamline control and potentially decrease the time and effort needed to
complete tasks. This preliminary study therefore suggests a way to naturally
integrate human- and machine-based decision making systems.
| Ann L. Edwards, Alexandra Kearney, Michael Rory Dawson, Richard S.
Sutton, Patrick M. Pilarski | null | 1309.4714 | null | null |
Network Anomaly Detection: A Survey and Comparative Analysis of
Stochastic and Deterministic Methods | stat.ML cs.LG cs.NI | We present five methods to the problem of network anomaly detection. These
methods cover most of the common techniques in the anomaly detection field,
including Statistical Hypothesis Tests (SHT), Support Vector Machines (SVM) and
clustering analysis. We evaluate all methods in a simulated network that
consists of nominal data, three flow-level anomalies and one packet-level
attack. Through analyzing the results, we point out the advantages and
disadvantages of each method and conclude that combining the results of the
individual methods can yield improved anomaly detection results.
| Jing Wang, Daniel Rossell, Christos G. Cassandras, and Ioannis Ch.
Paschalidis | null | 1309.4844 | null | null |
HOL(y)Hammer: Online ATP Service for HOL Light | cs.AI cs.DL cs.LG cs.LO cs.MS | HOL(y)Hammer is an online AI/ATP service for formal (computer-understandable)
mathematics encoded in the HOL Light system. The service allows its users to
upload and automatically process an arbitrary formal development (project)
based on HOL Light, and to attack arbitrary conjectures that use the concepts
defined in some of the uploaded projects. For that, the service uses several
automated reasoning systems combined with several premise selection methods
trained on all the project proofs. The projects that are readily available on
the server for such query answering include the recent versions of the
Flyspeck, Multivariate Analysis and Complex Analysis libraries. The service
runs on a 48-CPU server, currently employing in parallel for each task 7 AI/ATP
combinations and 4 decision procedures that contribute to its overall
performance. The system is also available for local installation by interested
users, who can customize it for their own proof development. An Emacs interface
allowing parallel asynchronous queries to the service is also provided. The
overall structure of the service is outlined, problems that arise and their
solutions are discussed, and an initial account of using the system is given.
| Cezary Kaliszyk and Josef Urban | null | 1309.4962 | null | null |
Bayesian rules and stochastic models for high accuracy prediction of
solar radiation | cs.LG stat.AP | It is essential to find solar predictive methods to massively insert
renewable energies on the electrical distribution grid. The goal of this study
is to find the best methodology allowing predicting with high accuracy the
hourly global radiation. The knowledge of this quantity is essential for the
grid manager or the private PV producer in order to anticipate fluctuations
related to clouds occurrences and to stabilize the injected PV power. In this
paper, we test both methodologies: single and hybrid predictors. In the first
class, we include the multi-layer perceptron (MLP), auto-regressive and moving
average (ARMA), and persistence models. In the second class, we mix these
predictors with Bayesian rules to obtain ad-hoc models selections, and Bayesian
averages of outputs related to single models. If MLP and ARMA are equivalent
(nRMSE close to 40.5% for the both), this hybridization allows a nRMSE gain
upper than 14 percentage points compared to the persistence estimation
(nRMSE=37% versus 51%).
| Cyril Voyant (SPE), C. Darras (SPE), Marc Muselli (SPE), Christophe
Paoli (SPE), Marie Laure Nivet (SPE), Philippe Poggi (SPE) | null | 1309.4999 | null | null |
A Comparative Analysis of Ensemble Classifiers: Case Studies in Genomics | cs.LG q-bio.GN stat.ML | The combination of multiple classifiers using ensemble methods is
increasingly important for making progress in a variety of difficult prediction
problems. We present a comparative analysis of several ensemble methods through
two case studies in genomics, namely the prediction of genetic interactions and
protein functions, to demonstrate their efficacy on real-world datasets and
draw useful conclusions about their behavior. These methods include simple
aggregation, meta-learning, cluster-based meta-learning, and ensemble selection
using heterogeneous classifiers trained on resampled data to improve the
diversity of their predictions. We present a detailed analysis of these methods
across 4 genomics datasets and find the best of these methods offer
statistically significant improvements over the state of the art in their
respective domains. In addition, we establish a novel connection between
ensemble selection and meta-learning, demonstrating how both of these disparate
methods establish a balance between ensemble diversity and performance.
| Sean Whalen and Gaurav Pandey | null | 1309.5047 | null | null |
Recognizing Speech in a Novel Accent: The Motor Theory of Speech
Perception Reframed | cs.CL cs.LG q-bio.NC | The motor theory of speech perception holds that we perceive the speech of
another in terms of a motor representation of that speech. However, when we
have learned to recognize a foreign accent, it seems plausible that recognition
of a word rarely involves reconstruction of the speech gestures of the speaker
rather than the listener. To better assess the motor theory and this
observation, we proceed in three stages. Part 1 places the motor theory of
speech perception in a larger framework based on our earlier models of the
adaptive formation of mirror neurons for grasping, and for viewing extensions
of that mirror system as part of a larger system for neuro-linguistic
processing, augmented by the present consideration of recognizing speech in a
novel accent. Part 2 then offers a novel computational model of how a listener
comes to understand the speech of someone speaking the listener's native
language with a foreign accent. The core tenet of the model is that the
listener uses hypotheses about the word the speaker is currently uttering to
update probabilities linking the sound produced by the speaker to phonemes in
the native language repertoire of the listener. This, on average, improves the
recognition of later words. This model is neutral regarding the nature of the
representations it uses (motor vs. auditory). It serve as a reference point for
the discussion in Part 3, which proposes a dual-stream neuro-linguistic
architecture to revisits claims for and against the motor theory of speech
perception and the relevance of mirror neurons, and extracts some implications
for the reframing of the motor theory.
| Cl\'ement Moulin-Frier (INRIA Bordeaux - Sud-Ouest, GIPSA-lab), M. A.
Arbib (USC) | 10.1007/s00422-013-0557-3 | 1309.5319 | null | null |
Latent Fisher Discriminant Analysis | cs.LG cs.CV stat.ML | Linear Discriminant Analysis (LDA) is a well-known method for dimensionality
reduction and classification. Previous studies have also extended the
binary-class case into multi-classes. However, many applications, such as
object detection and keyframe extraction cannot provide consistent
instance-label pairs, while LDA requires labels on instance level for training.
Thus it cannot be directly applied for semi-supervised classification problem.
In this paper, we overcome this limitation and propose a latent variable Fisher
discriminant analysis model. We relax the instance-level labeling into
bag-level, is a kind of semi-supervised (video-level labels of event type are
required for semantic frame extraction) and incorporates a data-driven prior
over the latent variables. Hence, our method combines the latent variable
inference and dimension reduction in an unified bayesian framework. We test our
method on MUSK and Corel data sets and yield competitive results compared to
the baseline approach. We also demonstrate its capacity on the challenging
TRECVID MED11 dataset for semantic keyframe extraction and conduct a
human-factors ranking-based experimental evaluation, which clearly demonstrates
our proposed method consistently extracts more semantically meaningful
keyframes than challenging baselines.
| Gang Chen | null | 1309.5427 | null | null |
Stochastic Bound Majorization | cs.LG | Recently a majorization method for optimizing partition functions of
log-linear models was proposed alongside a novel quadratic variational
upper-bound. In the batch setting, it outperformed state-of-the-art first- and
second-order optimization methods on various learning tasks. We propose a
stochastic version of this bound majorization method as well as a low-rank
modification for high-dimensional data-sets. The resulting stochastic
second-order method outperforms stochastic gradient descent (across variations
and various tunings) both in terms of the number of iterations and computation
time till convergence while finding a better quality parameter setting. The
proposed method bridges first- and second-order stochastic optimization methods
by maintaining a computational complexity that is linear in the data dimension
and while exploiting second order information about the pseudo-global curvature
of the objective function (as opposed to the local curvature in the Hessian).
| Anna Choromanska and Tony Jebara | null | 1309.5605 | null | null |
Multiple Instance Learning with Bag Dissimilarities | stat.ML cs.LG | Multiple instance learning (MIL) is concerned with learning from sets (bags)
of objects (instances), where the individual instance labels are ambiguous. In
this setting, supervised learning cannot be applied directly. Often,
specialized MIL methods learn by making additional assumptions about the
relationship of the bag labels and instance labels. Such assumptions may fit a
particular dataset, but do not generalize to the whole range of MIL problems.
Other MIL methods shift the focus of assumptions from the labels to the overall
(dis)similarity of bags, and therefore learn from bags directly. We propose to
represent each bag by a vector of its dissimilarities to other bags in the
training set, and treat these dissimilarities as a feature representation. We
show several alternatives to define a dissimilarity between bags and discuss
which definitions are more suitable for particular MIL problems. The
experimental results show that the proposed approach is computationally
inexpensive, yet very competitive with state-of-the-art algorithms on a wide
range of MIL datasets.
| Veronika Cheplygina, David M. J. Tax, and Marco Loog | 10.1016/j.patcog.2014.07.022 | 1309.5643 | null | null |
A new look at reweighted message passing | cs.AI cs.CV cs.LG | We propose a new family of message passing techniques for MAP estimation in
graphical models which we call {\em Sequential Reweighted Message Passing}
(SRMP). Special cases include well-known techniques such as {\em Min-Sum
Diffusion} (MSD) and a faster {\em Sequential Tree-Reweighted Message Passing}
(TRW-S). Importantly, our derivation is simpler than the original derivation of
TRW-S, and does not involve a decomposition into trees. This allows easy
generalizations. We present such a generalization for the case of higher-order
graphical models, and test it on several real-world problems with promising
results.
| Vladimir Kolmogorov | 10.1109/TPAMI.2014.2363465 | 1309.5655 | null | null |
Scalable Anomaly Detection in Large Homogenous Populations | cs.LG cs.DC cs.SY math.OC | Anomaly detection in large populations is a challenging but highly relevant
problem. The problem is essentially a multi-hypothesis problem, with a
hypothesis for every division of the systems into normal and anomal systems.
The number of hypothesis grows rapidly with the number of systems and
approximate solutions become a necessity for any problems of practical
interests. In the current paper we take an optimization approach to this
multi-hypothesis problem. We first observe that the problem is equivalent to a
non-convex combinatorial optimization problem. We then relax the problem to a
convex problem that can be solved distributively on the systems and that stays
computationally tractable as the number of systems increase. An interesting
property of the proposed method is that it can under certain conditions be
shown to give exactly the same result as the combinatorial multi-hypothesis
problem and the relaxation is hence tight.
| Henrik Ohlsson, Tianshi Chen, Sina Khoshfetrat Pakazad, Lennart Ljung
and S. Shankar Sastry | null | 1309.5803 | null | null |
A Kernel Classification Framework for Metric Learning | cs.LG | Learning a distance metric from the given training samples plays a crucial
role in many machine learning tasks, and various models and optimization
algorithms have been proposed in the past decade. In this paper, we generalize
several state-of-the-art metric learning methods, such as large margin nearest
neighbor (LMNN) and information theoretic metric learning (ITML), into a kernel
classification framework. First, doublets and triplets are constructed from the
training samples, and a family of degree-2 polynomial kernel functions are
proposed for pairs of doublets or triplets. Then, a kernel classification
framework is established, which can not only generalize many popular metric
learning methods such as LMNN and ITML, but also suggest new metric learning
methods, which can be efficiently implemented, interestingly, by using the
standard support vector machine (SVM) solvers. Two novel metric learning
methods, namely doublet-SVM and triplet-SVM, are then developed under the
proposed framework. Experimental results show that doublet-SVM and triplet-SVM
achieve competitive classification accuracies with state-of-the-art metric
learning methods such as ITML and LMNN but with significantly less training
time.
| Faqiang Wang, Wangmeng Zuo, Lei Zhang, Deyu Meng and David Zhang | null | 1309.5823 | null | null |
Demodulation of Sparse PPM Signals with Low Samples Using Trained RIP
Matrix | cs.OH cs.IT cs.LG math.IT | Compressed sensing (CS) theory considers the restricted isometry property
(RIP) as a sufficient condition for measurement matrix which guarantees the
recovery of any sparse signal from its compressed measurements. The RIP
condition also preserves enough information for classification of sparse
symbols, even with fewer measurements. In this work, we utilize RIP bound as
the cost function for training a simple neural network in order to exploit the
near optimal measurements or equivalently near optimal features for
classification of a known set of sparse symbols. As an example, we consider
demodulation of pulse position modulation (PPM) signals. The results indicate
that the proposed method has much better performance than the random
measurements and requires less samples than the optimum matched filter
demodulator, at the expense of some performance loss. Further, the proposed
approach does not need equalizer for multipath channels in contrast to the
conventional receiver.
| Seyed Hossein Hosseini, Mahrokh G. Shayesteh, Mehdi Chehel Amirani | null | 1309.5854 | null | null |
Fenchel Duals for Drifting Adversaries | cs.LG | We describe a primal-dual framework for the design and analysis of online
convex optimization algorithms for {\em drifting regret}. Existing literature
shows (nearly) optimal drifting regret bounds only for the $\ell_2$ and the
$\ell_1$-norms. Our work provides a connection between these algorithms and the
Online Mirror Descent ($\omd$) updates; one key insight that results from our
work is that in order for these algorithms to succeed, it suffices to have the
gradient of the regularizer to be bounded (in an appropriate norm). For
situations (like for the $\ell_1$ norm) where the vanilla regularizer does not
have this property, we have to {\em shift} the regularizer to ensure this.
Thus, this helps explain the various updates presented in \cite{bansal10,
buchbinder12}. We also consider the online variant of the problem with
1-lookahead, and with movement costs in the $\ell_2$-norm. Our primal dual
approach yields nearly optimal competitive ratios for this problem.
| Suman K Bera, Anamitra R Choudhury, Syamantak Das, Sambuddha Roy and
Jayram S. Thatchachar | null | 1309.5904 | null | null |
Feature Learning with Gaussian Restricted Boltzmann Machine for Robust
Speech Recognition | cs.CL cs.LG cs.SD | In this paper, we first present a new variant of Gaussian restricted
Boltzmann machine (GRBM) called multivariate Gaussian restricted Boltzmann
machine (MGRBM), with its definition and learning algorithm. Then we propose
using a learned GRBM or MGRBM to extract better features for robust speech
recognition. Our experiments on Aurora2 show that both GRBM-extracted and
MGRBM-extracted feature performs much better than Mel-frequency cepstral
coefficient (MFCC) with either HMM-GMM or hybrid HMM-deep neural network (DNN)
acoustic model, and MGRBM-extracted feature is slightly better.
| Xin Zheng, Zhiyong Wu, Helen Meng, Weifeng Li, Lianhong Cai | null | 1309.6176 | null | null |
Solving OSCAR regularization problems by proximal splitting algorithms | cs.CV cs.LG stat.ML | The OSCAR (octagonal selection and clustering algorithm for regression)
regularizer consists of a L_1 norm plus a pair-wise L_inf norm (responsible for
its grouping behavior) and was proposed to encourage group sparsity in
scenarios where the groups are a priori unknown. The OSCAR regularizer has a
non-trivial proximity operator, which limits its applicability. We reformulate
this regularizer as a weighted sorted L_1 norm, and propose its grouping
proximity operator (GPO) and approximate proximity operator (APO), thus making
state-of-the-art proximal splitting algorithms (PSAs) available to solve
inverse problems with OSCAR regularization. The GPO is in fact the APO followed
by additional grouping and averaging operations, which are costly in time and
storage, explaining the reason why algorithms with APO are much faster than
that with GPO. The convergences of PSAs with GPO are guaranteed since GPO is an
exact proximity operator. Although convergence of PSAs with APO is may not be
guaranteed, we have experimentally found that APO behaves similarly to GPO when
the regularization parameter of the pair-wise L_inf norm is set to an
appropriately small value. Experiments on recovery of group-sparse signals
(with unknown groups) show that PSAs with APO are very fast and accurate.
| Xiangrong Zeng and M\'ario A. T. Figueiredo | null | 1309.6301 | null | null |
A Unified Framework for Representation-based Subspace Clustering of
Out-of-sample and Large-scale Data | cs.LG cs.CV stat.ML | Under the framework of spectral clustering, the key of subspace clustering is
building a similarity graph which describes the neighborhood relations among
data points. Some recent works build the graph using sparse, low-rank, and
$\ell_2$-norm-based representation, and have achieved state-of-the-art
performance. However, these methods have suffered from the following two
limitations. First, the time complexities of these methods are at least
proportional to the cube of the data size, which make those methods inefficient
for solving large-scale problems. Second, they cannot cope with out-of-sample
data that are not used to construct the similarity graph. To cluster each
out-of-sample datum, the methods have to recalculate the similarity graph and
the cluster membership of the whole data set. In this paper, we propose a
unified framework which makes representation-based subspace clustering
algorithms feasible to cluster both out-of-sample and large-scale data. Under
our framework, the large-scale problem is tackled by converting it as
out-of-sample problem in the manner of "sampling, clustering, coding, and
classifying". Furthermore, we give an estimation for the error bounds by
treating each subspace as a point in a hyperspace. Extensive experimental
results on various benchmark data sets show that our methods outperform several
recently-proposed scalable methods in clustering large-scale data set.
| Xi Peng, Huajin Tang, Lei Zhang, Zhang Yi, and Shijie Xiao | 10.1109/TNNLS.2015.2490080 | 1309.6487 | null | null |
Should I Stay or Should I Go: Coordinating Biological Needs with
Continuously-updated Assessments of the Environment | cs.NE cs.LG q-bio.NC | This paper presents Wanderer, a model of how autonomous adaptive systems
coordinate internal biological needs with moment-by-moment assessments of the
probabilities of events in the external world. The extent to which Wanderer
moves about or explores its environment reflects the relative activations of
two competing motivational sub-systems: one represents the need to acquire
energy and it excites exploration, and the other represents the need to avoid
predators and it inhibits exploration. The environment contains food,
predators, and neutral stimuli. Wanderer responds to these events in a way that
is adaptive in the short turn, and reassesses the probabilities of these events
so that it can modify its long term behaviour appropriately. When food appears,
Wanderer be-comes satiated and exploration temporarily decreases. When a
predator appears, Wanderer both decreases exploration in the short term, and
becomes more "cautious" about exploring in the future. Wanderer also forms
associations between neutral features and salient ones (food and predators)
when they are present at the same time, and uses these associations to guide
its behaviour.
| Liane Gabora | null | 1309.6584 | null | null |
Distributed Online Learning in Social Recommender Systems | cs.SI cs.LG stat.ML | In this paper, we consider decentralized sequential decision making in
distributed online recommender systems, where items are recommended to users
based on their search query as well as their specific background including
history of bought items, gender and age, all of which comprise the context
information of the user. In contrast to centralized recommender systems, in
which there is a single centralized seller who has access to the complete
inventory of items as well as the complete record of sales and user
information, in decentralized recommender systems each seller/learner only has
access to the inventory of items and user information for its own products and
not the products and user information of other sellers, but can get commission
if it sells an item of another seller. Therefore the sellers must distributedly
find out for an incoming user which items to recommend (from the set of own
items or items of another seller), in order to maximize the revenue from own
sales and commissions. We formulate this problem as a cooperative contextual
bandit problem, analytically bound the performance of the sellers compared to
the best recommendation strategy given the complete realization of user
arrivals and the inventory of items, as well as the context-dependent purchase
probabilities of each item, and verify our results via numerical examples on a
distributed data set adapted based on Amazon data. We evaluate the dependence
of the performance of a seller on the inventory of items the seller has, the
number of connections it has with the other sellers, and the commissions which
the seller gets by selling items of other sellers to its users.
| Cem Tekin, Simpson Zhang, Mihaela van der Schaar | 10.1109/JSTSP.2014.2299517 | 1309.6707 | null | null |
One-class Collaborative Filtering with Random Graphs: Annotated Version | stat.ML cs.LG | The bane of one-class collaborative filtering is interpreting and modelling
the latent signal from the missing class. In this paper we present a novel
Bayesian generative model for implicit collaborative filtering. It forms a core
component of the Xbox Live architecture, and unlike previous approaches,
delineates the odds of a user disliking an item from simply not considering it.
The latent signal is treated as an unobserved random graph connecting users
with items they might have encountered. We demonstrate how large-scale
distributed learning can be achieved through a combination of stochastic
gradient descent and mean field variational inference over random graph
samples. A fine-grained comparison is done against a state of the art baseline
on real world data.
| Ulrich Paquet, Noam Koenigstein | null | 1309.6786 | null | null |
Generative Multiple-Instance Learning Models For Quantitative
Electromyography | cs.LG stat.ML | We present a comprehensive study of the use of generative modeling approaches
for Multiple-Instance Learning (MIL) problems. In MIL a learner receives
training instances grouped together into bags with labels for the bags only
(which might not be correct for the comprised instances). Our work was
motivated by the task of facilitating the diagnosis of neuromuscular disorders
using sets of motor unit potential trains (MUPTs) detected within a muscle
which can be cast as a MIL problem. Our approach leads to a state-of-the-art
solution to the problem of muscle classification. By introducing and analyzing
generative models for MIL in a general framework and examining a variety of
model structures and components, our work also serves as a methodological guide
to modelling MIL tasks. We evaluate our proposed methods both on MUPT datasets
and on the MUSK1 dataset, one of the most widely used benchmarks for MIL.
| Tameem Adel, Benn Smith, Ruth Urner, Daniel Stashuk, Daniel J. Lizotte | null | 1309.6811 | null | null |
The Bregman Variational Dual-Tree Framework | cs.LG stat.ML | Graph-based methods provide a powerful tool set for many non-parametric
frameworks in Machine Learning. In general, the memory and computational
complexity of these methods is quadratic in the number of examples in the data
which makes them quickly infeasible for moderate to large scale datasets. A
significant effort to find more efficient solutions to the problem has been
made in the literature. One of the state-of-the-art methods that has been
recently introduced is the Variational Dual-Tree (VDT) framework. Despite some
of its unique features, VDT is currently restricted only to Euclidean spaces
where the Euclidean distance quantifies the similarity. In this paper, we
extend the VDT framework beyond the Euclidean distance to more general Bregman
divergences that include the Euclidean distance as a special case. By
exploiting the properties of the general Bregman divergence, we show how the
new framework can maintain all the pivotal features of the VDT framework and
yet significantly improve its performance in non-Euclidean domains. We apply
the proposed framework to different text categorization problems and
demonstrate its benefits over the original VDT.
| Saeed Amizadeh, Bo Thiesson, Milos Hauskrecht | null | 1309.6812 | null | null |
Hinge-loss Markov Random Fields: Convex Inference for Structured
Prediction | cs.LG stat.ML | Graphical models for structured domains are powerful tools, but the
computational complexities of combinatorial prediction spaces can force
restrictions on models, or require approximate inference in order to be
tractable. Instead of working in a combinatorial space, we use hinge-loss
Markov random fields (HL-MRFs), an expressive class of graphical models with
log-concave density functions over continuous variables, which can represent
confidences in discrete predictions. This paper demonstrates that HL-MRFs are
general tools for fast and accurate structured prediction. We introduce the
first inference algorithm that is both scalable and applicable to the full
class of HL-MRFs, and show how to train HL-MRFs with several learning
algorithms. Our experiments show that HL-MRFs match or surpass the predictive
performance of state-of-the-art methods, including discrete models, in four
application domains.
| Stephen Bach, Bert Huang, Ben London, Lise Getoor | null | 1309.6813 | null | null |
High-dimensional Joint Sparsity Random Effects Model for Multi-task
Learning | cs.LG stat.ML | Joint sparsity regularization in multi-task learning has attracted much
attention in recent years. The traditional convex formulation employs the group
Lasso relaxation to achieve joint sparsity across tasks. Although this approach
leads to a simple convex formulation, it suffers from several issues due to the
looseness of the relaxation. To remedy this problem, we view jointly sparse
multi-task learning as a specialized random effects model, and derive a convex
relaxation approach that involves two steps. The first step learns the
covariance matrix of the coefficients using a convex formulation which we refer
to as sparse covariance coding; the second step solves a ridge regression
problem with a sparse quadratic regularizer based on the covariance matrix
obtained in the first step. It is shown that this approach produces an
asymptotically optimal quadratic regularizer in the multitask learning setting
when the number of tasks approaches infinity. Experimental results demonstrate
that the convex formulation obtained via the proposed model significantly
outperforms group Lasso (and related multi-stage formulations
| Krishnakumar Balasubramanian, Kai Yu, Tong Zhang | null | 1309.6814 | null | null |
Boosting in the presence of label noise | cs.LG stat.ML | Boosting is known to be sensitive to label noise. We studied two approaches
to improve AdaBoost's robustness against labelling errors. One is to employ a
label-noise robust classifier as a base learner, while the other is to modify
the AdaBoost algorithm to be more robust. Empirical evaluation shows that a
committee of robust classifiers, although converges faster than non label-noise
aware AdaBoost, is still susceptible to label noise. However, pairing it with
the new robust Boosting algorithm we propose here results in a more resilient
algorithm under mislabelling.
| Jakramate Bootkrajang, Ata Kaban | null | 1309.6818 | null | null |
Hilbert Space Embeddings of Predictive State Representations | cs.LG stat.ML | Predictive State Representations (PSRs) are an expressive class of models for
controlled stochastic processes. PSRs represent state as a set of predictions
of future observable events. Because PSRs are defined entirely in terms of
observable data, statistically consistent estimates of PSR parameters can be
learned efficiently by manipulating moments of observed training data. Most
learning algorithms for PSRs have assumed that actions and observations are
finite with low cardinality. In this paper, we generalize PSRs to infinite sets
of observations and actions, using the recent concept of Hilbert space
embeddings of distributions. The essence is to represent the state as a
nonparametric conditional embedding operator in a Reproducing Kernel Hilbert
Space (RKHS) and leverage recent work in kernel methods to estimate, predict,
and update the representation. We show that these Hilbert space embeddings of
PSRs are able to gracefully handle continuous actions and observations, and
that our learned models outperform competing system identification algorithms
on several prediction benchmarks.
| Byron Boots, Geoffrey Gordon, Arthur Gretton | null | 1309.6819 | null | null |
SparsityBoost: A New Scoring Function for Learning Bayesian Network
Structure | cs.LG cs.AI stat.ML | We give a new consistent scoring function for structure learning of Bayesian
networks. In contrast to traditional approaches to scorebased structure
learning, such as BDeu or MDL, the complexity penalty that we propose is
data-dependent and is given by the probability that a conditional independence
test correctly shows that an edge cannot exist. What really distinguishes this
new scoring function from earlier work is that it has the property of becoming
computationally easier to maximize as the amount of data increases. We prove a
polynomial sample complexity result, showing that maximizing this score is
guaranteed to correctly learn a structure with no false edges and a
distribution close to the generating distribution, whenever there exists a
Bayesian network which is a perfect map for the data generating distribution.
Although the new score can be used with any search algorithm, we give empirical
results showing that it is particularly effective when used together with a
linear programming relaxation approach to Bayesian network structure learning.
| Eliot Brenner, David Sontag | null | 1309.6820 | null | null |
Sample Complexity of Multi-task Reinforcement Learning | cs.LG stat.ML | Transferring knowledge across a sequence of reinforcement-learning tasks is
challenging, and has a number of important applications. Though there is
encouraging empirical evidence that transfer can improve performance in
subsequent reinforcement-learning tasks, there has been very little theoretical
analysis. In this paper, we introduce a new multi-task algorithm for a sequence
of reinforcement-learning tasks when each task is sampled independently from
(an unknown) distribution over a finite set of Markov decision processes whose
parameters are initially unknown. For this setting, we prove under certain
assumptions that the per-task sample complexity of exploration is reduced
significantly due to transfer compared to standard single-task algorithms. Our
multi-task algorithm also has the desired characteristic that it is guaranteed
not to exhibit negative transfer: in the worst case its per-task sample
complexity is comparable to the corresponding single-task algorithm.
| Emma Brunskill, Lihong Li | null | 1309.6821 | null | null |
Convex Relaxations of Bregman Divergence Clustering | cs.LG stat.ML | Although many convex relaxations of clustering have been proposed in the past
decade, current formulations remain restricted to spherical Gaussian or
discriminative models and are susceptible to imbalanced clusters. To address
these shortcomings, we propose a new class of convex relaxations that can be
flexibly applied to more general forms of Bregman divergence clustering. By
basing these new formulations on normalized equivalence relations we retain
additional control on relaxation quality, which allows improvement in
clustering quality. We furthermore develop optimization methods that improve
scalability by exploiting recent implicit matrix norm methods. In practice, we
find that the new formulations are able to efficiently produce tighter
clusterings that improve the accuracy of state of the art methods.
| Hao Cheng, Xinhua Zhang, Dale Schuurmans | null | 1309.6823 | null | null |
Bethe-ADMM for Tree Decomposition based Parallel MAP Inference | cs.AI cs.LG stat.ML | We consider the problem of maximum a posteriori (MAP) inference in discrete
graphical models. We present a parallel MAP inference algorithm called
Bethe-ADMM based on two ideas: tree-decomposition of the graph and the
alternating direction method of multipliers (ADMM). However, unlike the
standard ADMM, we use an inexact ADMM augmented with a Bethe-divergence based
proximal function, which makes each subproblem in ADMM easy to solve in
parallel using the sum-product algorithm. We rigorously prove global
convergence of Bethe-ADMM. The proposed algorithm is extensively evaluated on
both synthetic and real datasets to illustrate its effectiveness. Further, the
parallel Bethe-ADMM is shown to scale almost linearly with increasing number of
cores.
| Qiang Fu, Huahua Wang, Arindam Banerjee | null | 1309.6829 | null | null |
Building Bridges: Viewing Active Learning from the Multi-Armed Bandit
Lens | cs.LG stat.ML | In this paper we propose a multi-armed bandit inspired, pool based active
learning algorithm for the problem of binary classification. By carefully
constructing an analogy between active learning and multi-armed bandits, we
utilize ideas such as lower confidence bounds, and self-concordant
regularization from the multi-armed bandit literature to design our proposed
algorithm. Our algorithm is a sequential algorithm, which in each round assigns
a sampling distribution on the pool, samples one point from this distribution,
and queries the oracle for the label of this sampled point. The design of this
sampling distribution is also inspired by the analogy between active learning
and multi-armed bandits. We show how to derive lower confidence bounds required
by our algorithm. Experimental comparisons to previously proposed active
learning algorithms show superior performance on some standard UCI datasets.
| Ravi Ganti, Alexander G. Gray | null | 1309.6830 | null | null |
Batch-iFDD for Representation Expansion in Large MDPs | cs.LG stat.ML | Matching pursuit (MP) methods are a promising class of feature construction
algorithms for value function approximation. Yet existing MP methods require
creating a pool of potential features, mandating expert knowledge or
enumeration of a large feature pool, both of which hinder scalability. This
paper introduces batch incremental feature dependency discovery (Batch-iFDD) as
an MP method that inherits a provable convergence property. Additionally,
Batch-iFDD does not require a large pool of features, leading to lower
computational complexity. Empirical policy evaluation results across three
domains with up to one million states highlight the scalability of Batch-iFDD
over the previous state of the art MP algorithm.
| Alborz Geramifard, Thomas J. Walsh, Nicholas Roy, Jonathan How | null | 1309.6831 | null | null |
Multiple Instance Learning by Discriminative Training of Markov Networks | cs.LG stat.ML | We introduce a graphical framework for multiple instance learning (MIL) based
on Markov networks. This framework can be used to model the traditional MIL
definition as well as more general MIL definitions. Different levels of
ambiguity -- the portion of positive instances in a bag -- can be explored in
weakly supervised data. To train these models, we propose a discriminative
max-margin learning algorithm leveraging efficient inference for
cardinality-based cliques. The efficacy of the proposed framework is evaluated
on a variety of data sets. Experimental results verify that encoding or
learning the degree of ambiguity can improve classification performance.
| Hossein Hajimirsadeghi, Jinling Li, Greg Mori, Mohammad Zaki, Tarek
Sayed | null | 1309.6833 | null | null |
Unsupervised Learning of Noisy-Or Bayesian Networks | cs.LG stat.ML | This paper considers the problem of learning the parameters in Bayesian
networks of discrete variables with known structure and hidden variables.
Previous approaches in these settings typically use expectation maximization;
when the network has high treewidth, the required expectations might be
approximated using Monte Carlo or variational methods. We show how to avoid
inference altogether during learning by giving a polynomial-time algorithm
based on the method-of-moments, building upon recent work on learning
discrete-valued mixture models. In particular, we show how to learn the
parameters for a family of bipartite noisy-or Bayesian networks. In our
experimental results, we demonstrate an application of our algorithm to
learning QMR-DT, a large Bayesian network used for medical diagnosis. We show
that it is possible to fully learn the parameters of QMR-DT even when only the
findings are observed in the training data (ground truth diseases unknown).
| Yonatan Halpern, David Sontag | null | 1309.6834 | null | null |
Gaussian Processes for Big Data | cs.LG stat.ML | We introduce stochastic variational inference for Gaussian process models.
This enables the application of Gaussian process (GP) models to data sets
containing millions of data points. We show how GPs can be vari- ationally
decomposed to depend on a set of globally relevant inducing variables which
factorize the model in the necessary manner to perform variational inference.
Our ap- proach is readily extended to models with non-Gaussian likelihoods and
latent variable models based around Gaussian processes. We demonstrate the
approach on a simple toy problem and two real world data sets.
| James Hensman, Nicolo Fusi, Neil D. Lawrence | null | 1309.6835 | null | null |
Inverse Covariance Estimation for High-Dimensional Data in Linear Time
and Space: Spectral Methods for Riccati and Sparse Models | cs.LG stat.ML | We propose maximum likelihood estimation for learning Gaussian graphical
models with a Gaussian (ell_2^2) prior on the parameters. This is in contrast
to the commonly used Laplace (ell_1) prior for encouraging sparseness. We show
that our optimization problem leads to a Riccati matrix equation, which has a
closed form solution. We propose an efficient algorithm that performs a
singular value decomposition of the training data. Our algorithm is
O(NT^2)-time and O(NT)-space for N variables and T samples. Our method is
tailored to high-dimensional problems (N gg T), in which sparseness promoting
methods become intractable. Furthermore, instead of obtaining a single solution
for a specific regularization parameter, our algorithm finds the whole solution
path. We show that the method has logarithmic sample complexity under the
spiked covariance model. We also propose sparsification of the dense solution
with provable performance guarantees. We provide techniques for using our
learnt models, such as removing unimportant variables, computing likelihoods
and conditional distributions. Finally, we show promising results in several
gene expressions datasets.
| Jean Honorio, Tommi S. Jaakkola | null | 1309.6838 | null | null |
Constrained Bayesian Inference for Low Rank Multitask Learning | cs.LG stat.ML | We present a novel approach for constrained Bayesian inference. Unlike
current methods, our approach does not require convexity of the constraint set.
We reduce the constrained variational inference to a parametric optimization
over the feasible set of densities and propose a general recipe for such
problems. We apply the proposed constrained Bayesian inference approach to
multitask learning subject to rank constraints on the weight matrix. Further,
constrained parameter estimation is applied to recover the sparse conditional
independence structure encoded by prior precision matrices. Our approach is
motivated by reverse inference for high dimensional functional neuroimaging, a
domain where the high dimensionality and small number of examples requires the
use of constraints to ensure meaningful and effective models. For this
application, we propose a model that jointly learns a weight matrix and the
prior inverse covariance structure between different tasks. We present
experimental validation showing that the proposed approach outperforms strong
baseline models in terms of predictive performance and structure recovery.
| Oluwasanmi Koyejo, Joydeep Ghosh | null | 1309.6840 | null | null |
Learning Max-Margin Tree Predictors | cs.LG stat.ML | Structured prediction is a powerful framework for coping with joint
prediction of interacting outputs. A central difficulty in using this framework
is that often the correct label dependence structure is unknown. At the same
time, we would like to avoid an overly complex structure that will lead to
intractable prediction. In this work we address the challenge of learning tree
structured predictive models that achieve high accuracy while at the same time
facilitate efficient (linear time) inference. We start by proving that this
task is in general NP-hard, and then suggest an approximate alternative.
Briefly, our CRANK approach relies on a novel Circuit-RANK regularizer that
penalizes non-tree structures and that can be optimized using a CCCP procedure.
We demonstrate the effectiveness of our approach on several domains and show
that, despite the relative simplicity of the structure, prediction accuracy is
competitive with a fully connected model that is computationally costly at
prediction time.
| Ofer Meshi, Elad Eban, Gal Elidan, Amir Globerson | null | 1309.6847 | null | null |
Cyclic Causal Discovery from Continuous Equilibrium Data | cs.LG cs.AI stat.ML | We propose a method for learning cyclic causal models from a combination of
observational and interventional equilibrium data. Novel aspects of the
proposed method are its ability to work with continuous data (without assuming
linearity) and to deal with feedback loops. Within the context of biochemical
reactions, we also propose a novel way of modeling interventions that modify
the activity of compounds instead of their abundance. For computational
reasons, we approximate the nonlinear causal mechanisms by (coupled) local
linearizations, one for each experimental condition. We apply the method to
reconstruct a cellular signaling network from the flow cytometry data measured
by Sachs et al. (2005). We show that our method finds evidence in the data for
feedback loops and that it gives a more accurate quantitative description of
the data at comparable model complexity.
| Joris Mooij, Tom Heskes | null | 1309.6849 | null | null |
Structured Convex Optimization under Submodular Constraints | cs.LG cs.DS stat.ML | A number of discrete and continuous optimization problems in machine learning
are related to convex minimization problems under submodular constraints. In
this paper, we deal with a submodular function with a directed graph structure,
and we show that a wide range of convex optimization problems under submodular
constraints can be solved much more efficiently than general submodular
optimization methods by a reduction to a maximum flow problem. Furthermore, we
give some applications, including sparse optimization methods, in which the
proposed methods are effective. Additionally, we evaluate the performance of
the proposed method through computational experiments.
| Kiyohito Nagano, Yoshinobu Kawahara | null | 1309.6850 | null | null |
Treedy: A Heuristic for Counting and Sampling Subsets | cs.DS cs.AI cs.LG | Consider a collection of weighted subsets of a ground set N. Given a query
subset Q of N, how fast can one (1) find the weighted sum over all subsets of
Q, and (2) sample a subset of Q proportionally to the weights? We present a
tree-based greedy heuristic, Treedy, that for a given positive tolerance d
answers such counting and sampling queries to within a guaranteed relative
error d and total variation distance d, respectively. Experimental results on
artificial instances and in application to Bayesian structure discovery in
Bayesian networks show that approximations yield dramatic savings in running
time compared to exact computation, and that Treedy typically outperforms a
previously proposed sorting-based heuristic.
| Teppo Niinimaki, Mikko Koivisto | null | 1309.6851 | null | null |
Stochastic Rank Aggregation | cs.LG cs.IR stat.ML | This paper addresses the problem of rank aggregation, which aims to find a
consensus ranking among multiple ranking inputs. Traditional rank aggregation
methods are deterministic, and can be categorized into explicit and implicit
methods depending on whether rank information is explicitly or implicitly
utilized. Surprisingly, experimental results on real data sets show that
explicit rank aggregation methods would not work as well as implicit methods,
although rank information is critical for the task. Our analysis indicates that
the major reason might be the unreliable rank information from incomplete
ranking inputs. To solve this problem, we propose to incorporate uncertainty
into rank aggregation and tackle the problem in both unsupervised and
supervised scenario. We call this novel framework {stochastic rank aggregation}
(St.Agg for short). Specifically, we introduce a prior distribution on ranks,
and transform the ranking functions or objectives in traditional explicit
methods to their expectations over this distribution. Our experiments on
benchmark data sets show that the proposed St.Agg outperforms the baselines in
both unsupervised and supervised scenarios.
| Shuzi Niu, Yanyan Lan, Jiafeng Guo, Xueqi Cheng | null | 1309.6852 | null | null |
The Supervised IBP: Neighbourhood Preserving Infinite Latent Feature
Models | cs.LG stat.ML | We propose a probabilistic model to infer supervised latent variables in the
Hamming space from observed data. Our model allows simultaneous inference of
the number of binary latent variables, and their values. The latent variables
preserve neighbourhood structure of the data in a sense that objects in the
same semantic concept have similar latent values, and objects in different
concepts have dissimilar latent values. We formulate the supervised infinite
latent variable problem based on an intuitive principle of pulling objects
together if they are of the same type, and pushing them apart if they are not.
We then combine this principle with a flexible Indian Buffet Process prior on
the latent variables. We show that the inferred supervised latent variables can
be directly used to perform a nearest neighbour search for the purpose of
retrieval. We introduce a new application of dynamically extending hash codes,
and show how to effectively couple the structure of the hash codes with
continuously growing structure of the neighbourhood preserving infinite latent
feature space.
| Novi Quadrianto, Viktoriia Sharmanska, David A. Knowles, Zoubin
Ghahramani | null | 1309.6858 | null | null |
Identifying Finite Mixtures of Nonparametric Product Distributions and
Causal Inference of Confounders | cs.LG cs.AI stat.ML | We propose a kernel method to identify finite mixtures of nonparametric
product distributions. It is based on a Hilbert space embedding of the joint
distribution. The rank of the constructed tensor is equal to the number of
mixture components. We present an algorithm to recover the components by
partitioning the data points into clusters such that the variables are jointly
conditionally independent given the cluster. This method can be used to
identify finite confounders.
| Eleni Sgouritsa, Dominik Janzing, Jonas Peters, Bernhard Schoelkopf | null | 1309.6860 | null | null |
Determinantal Clustering Processes - A Nonparametric Bayesian Approach
to Kernel Based Semi-Supervised Clustering | cs.LG stat.ML | Semi-supervised clustering is the task of clustering data points into
clusters where only a fraction of the points are labelled. The true number of
clusters in the data is often unknown and most models require this parameter as
an input. Dirichlet process mixture models are appealing as they can infer the
number of clusters from the data. However, these models do not deal with high
dimensional data well and can encounter difficulties in inference. We present a
novel nonparameteric Bayesian kernel based method to cluster data points
without the need to prespecify the number of clusters or to model complicated
densities from which data points are assumed to be generated from. The key
insight is to use determinants of submatrices of a kernel matrix as a measure
of how close together a set of points are. We explore some theoretical
properties of the model and derive a natural Gibbs based algorithm with MCMC
hyperparameter learning. The model is implemented on a variety of synthetic and
real world data sets.
| Amar Shah, Zoubin Ghahramani | null | 1309.6862 | null | null |
Sparse Nested Markov models with Log-linear Parameters | cs.LG cs.AI stat.ML | Hidden variables are ubiquitous in practical data analysis, and therefore
modeling marginal densities and doing inference with the resulting models is an
important problem in statistics, machine learning, and causal inference.
Recently, a new type of graphical model, called the nested Markov model, was
developed which captures equality constraints found in marginals of directed
acyclic graph (DAG) models. Some of these constraints, such as the so called
`Verma constraint', strictly generalize conditional independence. To make
modeling and inference with nested Markov models practical, it is necessary to
limit the number of parameters in the model, while still correctly capturing
the constraints in the marginal of a DAG model. Placing such limits is similar
in spirit to sparsity methods for undirected graphical models, and regression
models. In this paper, we give a log-linear parameterization which allows
sparse modeling with nested Markov models. We illustrate the advantages of this
parameterization with a simulation study.
| Ilya Shpitser, Robin J. Evans, Thomas S. Richardson, James M. Robins | null | 1309.6863 | null | null |
Modeling Documents with Deep Boltzmann Machines | cs.LG cs.IR stat.ML | We introduce a Deep Boltzmann Machine model suitable for modeling and
extracting latent semantic representations from a large unstructured collection
of documents. We overcome the apparent difficulty of training a DBM with
judicious parameter tying. This parameter tying enables an efficient
pretraining algorithm and a state initialization scheme that aids inference.
The model can be trained just as efficiently as a standard Restricted Boltzmann
Machine. Our experiments show that the model assigns better log probability to
unseen data than the Replicated Softmax model. Features extracted from our
model outperform LDA, Replicated Softmax, and DocNADE models on document
retrieval and document classification tasks.
| Nitish Srivastava, Ruslan R Salakhutdinov, Geoffrey E. Hinton | null | 1309.6865 | null | null |
Speedy Model Selection (SMS) for Copula Models | cs.LG stat.ME | We tackle the challenge of efficiently learning the structure of expressive
multivariate real-valued densities of copula graphical models. We start by
theoretically substantiating the conjecture that for many copula families the
magnitude of Spearman's rank correlation coefficient is monotone in the
expected contribution of an edge in network, namely the negative copula
entropy. We then build on this theory and suggest a novel Bayesian approach
that makes use of a prior over values of Spearman's rho for learning
copula-based models that involve a mix of copula families. We demonstrate the
generalization effectiveness of our highly efficient approach on sizable and
varied real-life datasets.
| Yaniv Tenzer, Gal Elidan | null | 1309.6867 | null | null |
Approximate Kalman Filter Q-Learning for Continuous State-Space MDPs | cs.LG stat.ML | We seek to learn an effective policy for a Markov Decision Process (MDP) with
continuous states via Q-Learning. Given a set of basis functions over state
action pairs we search for a corresponding set of linear weights that minimizes
the mean Bellman residual. Our algorithm uses a Kalman filter model to estimate
those weights and we have developed a simpler approximate Kalman filter model
that outperforms the current state of the art projected TD-Learning methods on
several standard benchmark problems.
| Charles Tripp, Ross D. Shachter | null | 1309.6868 | null | null |
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