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5,200 | A kernel-based framework for learning graded relations from data | stat.ML | Driven by a large number of potential applications in areas like
bioinformatics, information retrieval and social network analysis, the problem
setting of inferring relations between pairs of data objects has recently been
investigated quite intensively in the machine learning community. To this end,
current approaches... | computer science |
5,201 | Structure Learning of Probabilistic Graphical Models: A Comprehensive
Survey | stat.ML | Probabilistic graphical models combine the graph theory and probability
theory to give a multivariate statistical modeling. They provide a unified
description of uncertainty using probability and complexity using the graphical
model. Especially, graphical models provide the following several useful
properties:
- Grap... | computer science |
5,202 | Random Forests for Metric Learning with Implicit Pairwise Position
Dependence | stat.ML | Metric learning makes it plausible to learn distances for complex
distributions of data from labeled data. However, to date, most metric learning
methods are based on a single Mahalanobis metric, which cannot handle
heterogeneous data well. Those that learn multiple metrics throughout the space
have demonstrated superi... | computer science |
5,203 | Constrained variable clustering and the best basis problem in functional
data analysis | stat.ML | Functional data analysis involves data described by regular functions rather
than by a finite number of real valued variables. While some robust data
analysis methods can be applied directly to the very high dimensional vectors
obtained from a fine grid sampling of functional data, all methods benefit from
a prior simp... | computer science |
5,204 | Clustering Dynamic Web Usage Data | stat.ML | Most classification methods are based on the assumption that data conforms to
a stationary distribution. The machine learning domain currently suffers from a
lack of classification techniques that are able to detect the occurrence of a
change in the underlying data distribution. Ignoring possible changes in the
underly... | computer science |
5,205 | The Interaction of Entropy-Based Discretization and Sample Size: An
Empirical Study | stat.ML | An empirical investigation of the interaction of sample size and
discretization - in this case the entropy-based method CAIM (Class-Attribute
Interdependence Maximization) - was undertaken to evaluate the impact and
potential bias introduced into data mining performance metrics due to variation
in sample size as it imp... | computer science |
5,206 | Sparse Reward Processes | cs.LG | We introduce a class of learning problems where the agent is presented with a
series of tasks. Intuitively, if there is relation among those tasks, then the
information gained during execution of one task has value for the execution of
another task. Consequently, the agent is intrinsically motivated to explore its
envi... | computer science |
5,207 | Spike-and-Slab Sparse Coding for Unsupervised Feature Discovery | stat.ML | We consider the problem of using a factor model we call {\em spike-and-slab
sparse coding} (S3C) to learn features for a classification task. The S3C model
resembles both the spike-and-slab RBM and sparse coding. Since exact inference
in this model is intractable, we derive a structured variational inference
procedure ... | computer science |
5,208 | A metric learning perspective of SVM: on the relation of SVM and LMNN | cs.LG | Support Vector Machines, SVMs, and the Large Margin Nearest Neighbor
algorithm, LMNN, are two very popular learning algorithms with quite different
learning biases. In this paper we bring them into a unified view and show that
they have a much stronger relation than what is commonly thought. We analyze
SVMs from a metr... | computer science |
5,209 | On Constrained Spectral Clustering and Its Applications | cs.LG | Constrained clustering has been well-studied for algorithms such as $K$-means
and hierarchical clustering. However, how to satisfy many constraints in these
algorithmic settings has been shown to be intractable. One alternative to
encode many constraints is to use spectral clustering, which remains a
developing area. I... | computer science |
5,210 | Inference in Hidden Markov Models with Explicit State Duration
Distributions | stat.ML | In this letter we borrow from the inference techniques developed for
unbounded state-cardinality (nonparametric) variants of the HMM and use them to
develop a tuning-parameter free, black-box inference procedure for
Explicit-state-duration hidden Markov models (EDHMM). EDHMMs are HMMs that have
latent states consisting... | computer science |
5,211 | Fast Reinforcement Learning with Large Action Sets using
Error-Correcting Output Codes for MDP Factorization | cs.LG | The use of Reinforcement Learning in real-world scenarios is strongly limited
by issues of scale. Most RL learning algorithms are unable to deal with
problems composed of hundreds or sometimes even dozens of possible actions, and
therefore cannot be applied to many real-world problems. We consider the RL
problem in the... | computer science |
5,212 | A Method of Moments for Mixture Models and Hidden Markov Models | cs.LG | Mixture models are a fundamental tool in applied statistics and machine
learning for treating data taken from multiple subpopulations. The current
practice for estimating the parameters of such models relies on local search
heuristics (e.g., the EM algorithm) which are prone to failure, and existing
consistent methods ... | computer science |
5,213 | Multiple Operator-valued Kernel Learning | stat.ML | Positive definite operator-valued kernels generalize the well-known notion of
reproducing kernels, and are naturally adapted to multi-output learning
situations. This paper addresses the problem of learning a finite linear
combination of infinite-dimensional operator-valued kernels which are suitable
for extending func... | computer science |
5,214 | Regret Bounds for Deterministic Gaussian Process Bandits | cs.LG | This paper analyses the problem of Gaussian process (GP) bandits with
deterministic observations. The analysis uses a branch and bound algorithm that
is related to the UCB algorithm of (Srinivas et al., 2010). For GPs with
Gaussian observation noise, with variance strictly greater than zero, (Srinivas
et al., 2010) pro... | computer science |
5,215 | Differential Privacy for Functions and Functional Data | stat.ML | Differential privacy is a framework for privately releasing summaries of a
database. Previous work has focused mainly on methods for which the output is a
finite dimensional vector, or an element of some discrete set. We develop
methods for releasing functions while preserving differential privacy.
Specifically, we sho... | computer science |
5,216 | Robust Metric Learning by Smooth Optimization | cs.LG | Most existing distance metric learning methods assume perfect side
information that is usually given in pairwise or triplet constraints. Instead,
in many real-world applications, the constraints are derived from side
information, such as users' implicit feedbacks and citations among articles. As
a result, these constra... | computer science |
5,217 | Gaussian Process Topic Models | cs.LG | We introduce Gaussian Process Topic Models (GPTMs), a new family of topic
models which can leverage a kernel among documents while extracting correlated
topics. GPTMs can be considered a systematic generalization of the Correlated
Topic Models (CTMs) using ideas from Gaussian Process (GP) based embedding.
Since GPTMs w... | computer science |
5,218 | Bayesian Rose Trees | cs.LG | Hierarchical structure is ubiquitous in data across many domains. There are
many hierarchical clustering methods, frequently used by domain experts, which
strive to discover this structure. However, most of these methods limit
discoverable hierarchies to those with binary branching structure. This
limitation, while com... | computer science |
5,219 | Super-Samples from Kernel Herding | cs.LG | We extend the herding algorithm to continuous spaces by using the kernel
trick. The resulting "kernel herding" algorithm is an infinite memory
deterministic process that learns to approximate a PDF with a collection of
samples. We show that kernel herding decreases the error of expectations of
functions in the Hilbert ... | computer science |
5,220 | Inferring deterministic causal relations | cs.LG | We consider two variables that are related to each other by an invertible
function. While it has previously been shown that the dependence structure of
the noise can provide hints to determine which of the two variables is the
cause, we presently show that even in the deterministic (noise-free) case,
there are asymmetr... | computer science |
5,221 | Inference-less Density Estimation using Copula Bayesian Networks | cs.LG | We consider learning continuous probabilistic graphical models in the face of
missing data. For non-Gaussian models, learning the parameters and structure of
such models depends on our ability to perform efficient inference, and can be
prohibitive even for relatively modest domains. Recently, we introduced the
Copula B... | computer science |
5,222 | Regularized Maximum Likelihood for Intrinsic Dimension Estimation | cs.LG | We propose a new method for estimating the intrinsic dimension of a dataset
by applying the principle of regularized maximum likelihood to the distances
between close neighbors. We propose a regularization scheme which is motivated
by divergence minimization principles. We derive the estimator by a Poisson
process appr... | computer science |
5,223 | The Hierarchical Dirichlet Process Hidden Semi-Markov Model | cs.LG | There is much interest in the Hierarchical Dirichlet Process Hidden Markov
Model (HDP-HMM) as a natural Bayesian nonparametric extension of the
traditional HMM. However, in many settings the HDP-HMM's strict Markovian
constraints are undesirable, particularly if we wish to learn or encode
non-geometric state durations.... | computer science |
5,224 | Combining Spatial and Telemetric Features for Learning Animal Movement
Models | cs.LG | We introduce a new graphical model for tracking radio-tagged animals and
learning their movement patterns. The model provides a principled way to
combine radio telemetry data with an arbitrary set of userdefined, spatial
features. We describe an efficient stochastic gradient algorithm for fitting
model parameters to da... | computer science |
5,225 | Bayesian exponential family projections for coupled data sources | cs.LG | Exponential family extensions of principal component analysis (EPCA) have
received a considerable amount of attention in recent years, demonstrating the
growing need for basic modeling tools that do not assume the squared loss or
Gaussian distribution. We extend the EPCA model toolbox by presenting the first
exponentia... | computer science |
5,226 | Robust LogitBoost and Adaptive Base Class (ABC) LogitBoost | cs.LG | Logitboost is an influential boosting algorithm for classification. In this
paper, we develop robust logitboost to provide an explicit formulation of
tree-split criterion for building weak learners (regression trees) for
logitboost. This formulation leads to a numerically stable implementation of
logitboost. We then pr... | computer science |
5,227 | Approximating Higher-Order Distances Using Random Projections | cs.LG | We provide a simple method and relevant theoretical analysis for efficiently
estimating higher-order lp distances. While the analysis mainly focuses on l4,
our methodology extends naturally to p = 6,8,10..., (i.e., when p is even).
Distance-based methods are popular in machine learning. In large-scale
applications, sto... | computer science |
5,228 | Negative Tree Reweighted Belief Propagation | cs.LG | We introduce a new class of lower bounds on the log partition function of a
Markov random field which makes use of a reversed Jensen's inequality. In
particular, our method approximates the intractable distribution using a linear
combination of spanning trees with negative weights. This technique is a
lower-bound count... | computer science |
5,229 | Parameter-Free Spectral Kernel Learning | cs.LG | Due to the growing ubiquity of unlabeled data, learning with unlabeled data
is attracting increasing attention in machine learning. In this paper, we
propose a novel semi-supervised kernel learning method which can seamlessly
combine manifold structure of unlabeled data and Regularized Least-Squares
(RLS) to learn a ne... | computer science |
5,230 | Dirichlet Process Mixtures of Generalized Mallows Models | cs.LG | We present a Dirichlet process mixture model over discrete incomplete
rankings and study two Gibbs sampling inference techniques for estimating
posterior clusterings. The first approach uses a slice sampling subcomponent
for estimating cluster parameters. The second approach marginalizes out several
cluster parameters ... | computer science |
5,231 | Parametric Return Density Estimation for Reinforcement Learning | cs.LG | Most conventional Reinforcement Learning (RL) algorithms aim to optimize
decision-making rules in terms of the expected returns. However, especially for
risk management purposes, other risk-sensitive criteria such as the
value-at-risk or the expected shortfall are sometimes preferred in real
applications. Here, we desc... | computer science |
5,232 | A Family of Computationally Efficient and Simple Estimators for
Unnormalized Statistical Models | cs.LG | We introduce a new family of estimators for unnormalized statistical models.
Our family of estimators is parameterized by two nonlinear functions and uses a
single sample from an auxiliary distribution, generalizing Maximum Likelihood
Monte Carlo estimation of Geyer and Thompson (1992). The family is such that we
can e... | computer science |
5,233 | Sparse-posterior Gaussian Processes for general likelihoods | cs.LG | Gaussian processes (GPs) provide a probabilistic nonparametric representation
of functions in regression, classification, and other problems. Unfortunately,
exact learning with GPs is intractable for large datasets. A variety of
approximate GP methods have been proposed that essentially map the large
dataset into a sma... | computer science |
5,234 | A Bayesian Matrix Factorization Model for Relational Data | cs.LG | Relational learning can be used to augment one data source with other
correlated sources of information, to improve predictive accuracy. We frame a
large class of relational learning problems as matrix factorization problems,
and propose a hierarchical Bayesian model. Training our Bayesian model using
random-walk Metro... | computer science |
5,235 | Learning networks determined by the ratio of prior and data | cs.LG | Recent reports have described that the equivalent sample size (ESS) in a
Dirichlet prior plays an important role in learning Bayesian networks. This
paper provides an asymptotic analysis of the marginal likelihood score for a
Bayesian network. Results show that the ratio of the ESS and sample size
determine the penalty... | computer science |
5,236 | Online Semi-Supervised Learning on Quantized Graphs | cs.LG | In this paper, we tackle the problem of online semi-supervised learning
(SSL). When data arrive in a stream, the dual problems of computation and data
storage arise for any SSL method. We propose a fast approximate online SSL
algorithm that solves for the harmonic solution on an approximate graph. We
show, both empiric... | computer science |
5,237 | Speeding up the binary Gaussian process classification | stat.ML | Gaussian processes (GP) are attractive building blocks for many probabilistic
models. Their drawbacks, however, are the rapidly increasing inference time and
memory requirement alongside increasing data. The problem can be alleviated
with compactly supported (CS) covariance functions, which produce sparse
covariance ma... | computer science |
5,238 | Learning Structural Changes of Gaussian Graphical Models in Controlled
Experiments | cs.LG | Graphical models are widely used in scienti fic and engineering research to
represent conditional independence structures between random variables. In many
controlled experiments, environmental changes or external stimuli can often
alter the conditional dependence between the random variables, and potentially
produce s... | computer science |
5,239 | Source Separation and Higher-Order Causal Analysis of MEG and EEG | cs.LG | Separation of the sources and analysis of their connectivity have been an
important topic in EEG/MEG analysis. To solve this problem in an automatic
manner, we propose a two-layer model, in which the sources are conditionally
uncorrelated from each other, but not independent; the dependence is caused by
the causality i... | computer science |
5,240 | Invariant Gaussian Process Latent Variable Models and Application in
Causal Discovery | cs.LG | In nonlinear latent variable models or dynamic models, if we consider the
latent variables as confounders (common causes), the noise dependencies imply
further relations between the observed variables. Such models are then closely
related to causal discovery in the presence of nonlinear confounders, which is
a challeng... | computer science |
5,241 | Semi-Supervised Single- and Multi-Domain Regression with Multi-Domain
Training | stat.ML | We address the problems of multi-domain and single-domain regression based on
distinct and unpaired labeled training sets for each of the domains and a large
unlabeled training set from all domains. We formulate these problems as a
Bayesian estimation with partial knowledge of statistical relations. We propose
a worst-... | computer science |
5,242 | A Novel Training Algorithm for HMMs with Partial and Noisy Access to the
States | cs.LG | This paper proposes a new estimation algorithm for the parameters of an HMM
as to best account for the observed data. In this model, in addition to the
observation sequence, we have \emph{partial} and \emph{noisy} access to the
hidden state sequence as side information. This access can be seen as "partial
labeling" of ... | computer science |
5,243 | $k$-MLE: A fast algorithm for learning statistical mixture models | cs.LG | We describe $k$-MLE, a fast and efficient local search algorithm for learning
finite statistical mixtures of exponential families such as Gaussian mixture
models. Mixture models are traditionally learned using the
expectation-maximization (EM) soft clustering technique that monotonically
increases the incomplete (expec... | computer science |
5,244 | A Regularization Approach for Prediction of Edges and Node Features in
Dynamic Graphs | cs.LG | We consider the two problems of predicting links in a dynamic graph sequence
and predicting functions defined at each node of the graph. In many
applications, the solution of one problem is useful for solving the other.
Indeed, if these functions reflect node features, then they are related through
the graph structure.... | computer science |
5,245 | Spectral dimensionality reduction for HMMs | stat.ML | Hidden Markov Models (HMMs) can be accurately approximated using
co-occurrence frequencies of pairs and triples of observations by using a fast
spectral method in contrast to the usual slow methods like EM or Gibbs
sampling. We provide a new spectral method which significantly reduces the
number of model parameters tha... | computer science |
5,246 | A Lipschitz Exploration-Exploitation Scheme for Bayesian Optimization | cs.LG | The problem of optimizing unknown costly-to-evaluate functions has been
studied for a long time in the context of Bayesian Optimization. Algorithms in
this field aim to find the optimizer of the function by asking only a few
function evaluations at locations carefully selected based on a posterior
model. In this paper,... | computer science |
5,247 | Distribution-Dependent Sample Complexity of Large Margin Learning | stat.ML | We obtain a tight distribution-specific characterization of the sample
complexity of large-margin classification with L2 regularization: We introduce
the margin-adapted dimension, which is a simple function of the second order
statistics of the data distribution, and show distribution-specific upper and
lower bounds on... | computer science |
5,248 | UCB Algorithm for Exponential Distributions | stat.ML | We introduce in this paper a new algorithm for Multi-Armed Bandit (MAB)
problems. A machine learning paradigm popular within Cognitive Network related
topics (e.g., Spectrum Sensing and Allocation). We focus on the case where the
rewards are exponentially distributed, which is common when dealing with
Rayleigh fading c... | computer science |
5,249 | Asymptotic Accuracy of Distribution-Based Estimation for Latent
Variables | stat.ML | Hierarchical statistical models are widely employed in information science
and data engineering. The models consist of two types of variables: observable
variables that represent the given data and latent variables for the
unobservable labels. An asymptotic analysis of the models plays an important
role in evaluating t... | computer science |
5,250 | Efficient Protocols for Distributed Classification and Optimization | cs.LG | In distributed learning, the goal is to perform a learning task over data
distributed across multiple nodes with minimal (expensive) communication. Prior
work (Daume III et al., 2012) proposes a general model that bounds the
communication required for learning classifiers while allowing for $\eps$
training error on lin... | computer science |
5,251 | Regret in Online Combinatorial Optimization | cs.LG | We address online linear optimization problems when the possible actions of
the decision maker are represented by binary vectors. The regret of the
decision maker is the difference between her realized loss and the best loss
she would have achieved by picking, in hindsight, the best possible action. Our
goal is to unde... | computer science |
5,252 | Sparse Prediction with the $k$-Support Norm | stat.ML | We derive a novel norm that corresponds to the tightest convex relaxation of
sparsity combined with an $\ell_2$ penalty. We show that this new {\em
$k$-support norm} provides a tighter relaxation than the elastic net and is
thus a good replacement for the Lasso or the elastic net in sparse prediction
problems. Through ... | computer science |
5,253 | Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit
Problems | cs.LG | Multi-armed bandit problems are the most basic examples of sequential
decision problems with an exploration-exploitation trade-off. This is the
balance between staying with the option that gave highest payoffs in the past
and exploring new options that might give higher payoffs in the future.
Although the study of band... | computer science |
5,254 | Dissimilarity Clustering by Hierarchical Multi-Level Refinement | stat.ML | We introduce in this paper a new way of optimizing the natural extension of
the quantization error using in k-means clustering to dissimilarity data. The
proposed method is based on hierarchical clustering analysis combined with
multi-level heuristic refinement. The method is computationally efficient and
achieves bett... | computer science |
5,255 | A Conjugate Property between Loss Functions and Uncertainty Sets in
Classification Problems | stat.ML | In binary classification problems, mainly two approaches have been proposed;
one is loss function approach and the other is uncertainty set approach. The
loss function approach is applied to major learning algorithms such as support
vector machine (SVM) and boosting methods. The loss function represents the
penalty of ... | computer science |
5,256 | A Spectral Algorithm for Latent Dirichlet Allocation | cs.LG | The problem of topic modeling can be seen as a generalization of the
clustering problem, in that it posits that observations are generated due to
multiple latent factors (e.g., the words in each document are generated as a
mixture of several active topics, as opposed to just one). This increased
representational power ... | computer science |
5,257 | Oracle inequalities for computationally adaptive model selection | stat.ML | We analyze general model selection procedures using penalized empirical loss
minimization under computational constraints. While classical model selection
approaches do not consider computational aspects of performing model selection,
we argue that any practical model selection procedure must not only trade off
estimat... | computer science |
5,258 | Multidimensional Membership Mixture Models | cs.LG | We present the multidimensional membership mixture (M3) models where every
dimension of the membership represents an independent mixture model and each
data point is generated from the selected mixture components jointly. This is
helpful when the data has a certain shared structure. For example, three unique
means and ... | computer science |
5,259 | On the Consistency of AUC Pairwise Optimization | cs.LG | AUC (area under ROC curve) is an important evaluation criterion, which has
been popularly used in many learning tasks such as class-imbalance learning,
cost-sensitive learning, learning to rank, etc. Many learning approaches try to
optimize AUC, while owing to the non-convexity and discontinuousness of AUC,
almost all ... | computer science |
5,260 | Cross-conformal predictors | stat.ML | This note introduces the method of cross-conformal prediction, which is a
hybrid of the methods of inductive conformal prediction and cross-validation,
and studies its validity and predictive efficiency empirically. | computer science |
5,261 | Learning Theory Approach to Minimum Error Entropy Criterion | cs.LG | We consider the minimum error entropy (MEE) criterion and an empirical risk
minimization learning algorithm in a regression setting. A learning theory
approach is presented for this MEE algorithm and explicit error bounds are
provided in terms of the approximation ability and capacity of the involved
hypothesis space w... | computer science |
5,262 | How to sample if you must: on optimal functional sampling | stat.ML | We examine a fundamental problem that models various active sampling setups,
such as network tomography. We analyze sampling of a multivariate normal
distribution with an unknown expectation that needs to be estimated: in our
setup it is possible to sample the distribution from a given set of linear
functionals, and th... | computer science |
5,263 | Path Integral Control by Reproducing Kernel Hilbert Space Embedding | cs.LG | We present an embedding of stochastic optimal control problems, of the so
called path integral form, into reproducing kernel Hilbert spaces. Using
consistent, sample based estimates of the embedding leads to a model free,
non-parametric approach for calculation of an approximate solution to the
control problem. This fo... | computer science |
5,264 | Asymptotic Generalization Bound of Fisher's Linear Discriminant Analysis | stat.ML | Fisher's linear discriminant analysis (FLDA) is an important dimension
reduction method in statistical pattern recognition. It has been shown that
FLDA is asymptotically Bayes optimal under the homoscedastic Gaussian
assumption. However, this classical result has the following two major
limitations: 1) it holds only fo... | computer science |
5,265 | Structured Prediction Cascades | stat.ML | Structured prediction tasks pose a fundamental trade-off between the need for
model complexity to increase predictive power and the limited computational
resources for inference in the exponentially-sized output spaces such models
require. We formulate and develop the Structured Prediction Cascade
architecture: a seque... | computer science |
5,266 | Distance Metric Learning for Kernel Machines | stat.ML | Recent work in metric learning has significantly improved the
state-of-the-art in k-nearest neighbor classification. Support vector machines
(SVM), particularly with RBF kernels, are amongst the most popular
classification algorithms that uses distance metrics to compare examples. This
paper provides an empirical analy... | computer science |
5,267 | Online Learning with Predictable Sequences | stat.ML | We present methods for online linear optimization that take advantage of
benign (as opposed to worst-case) sequences. Specifically if the sequence
encountered by the learner is described well by a known "predictable process",
the algorithms presented enjoy tighter bounds as compared to the typical worst
case bounds. Ad... | computer science |
5,268 | Semi-supervised Clustering Ensemble by Voting | cs.LG | Clustering ensemble is one of the most recent advances in unsupervised
learning. It aims to combine the clustering results obtained using different
algorithms or from different runs of the same clustering algorithm for the same
data set, this is accomplished using on a consensus function, the efficiency
and accuracy of... | computer science |
5,269 | Changepoint detection for high-dimensional time series with missing data | stat.ML | This paper describes a novel approach to change-point detection when the
observed high-dimensional data may have missing elements. The performance of
classical methods for change-point detection typically scales poorly with the
dimensionality of the data, so that a large number of observations are
collected after the t... | computer science |
5,270 | A Widely Applicable Bayesian Information Criterion | cs.LG | A statistical model or a learning machine is called regular if the map taking
a parameter to a probability distribution is one-to-one and if its Fisher
information matrix is always positive definite. If otherwise, it is called
singular. In regular statistical models, the Bayes free energy, which is
defined by the minus... | computer science |
5,271 | Statistically adaptive learning for a general class of cost functions
(SA L-BFGS) | cs.LG | We present a system that enables rapid model experimentation for tera-scale
machine learning with trillions of non-zero features, billions of training
examples, and millions of parameters. Our contribution to the literature is a
new method (SA L-BFGS) for changing batch L-BFGS to perform in near real-time
by using stat... | computer science |
5,272 | Efficient EM Training of Gaussian Mixtures with Missing Data | cs.LG | In data-mining applications, we are frequently faced with a large fraction of
missing entries in the data matrix, which is problematic for most discriminant
machine learning algorithms. A solution that we explore in this paper is the
use of a generative model (a mixture of Gaussians) to compute the conditional
expectat... | computer science |
5,273 | Sparse coding for multitask and transfer learning | cs.LG | We investigate the use of sparse coding and dictionary learning in the
context of multitask and transfer learning. The central assumption of our
learning method is that the tasks parameters are well approximated by sparse
linear combinations of the atoms of a dictionary on a high or infinite
dimensional space. This ass... | computer science |
5,274 | Learning Probability Measures with respect to Optimal Transport Metrics | cs.LG | We study the problem of estimating, in the sense of optimal transport
metrics, a measure which is assumed supported on a manifold embedded in a
Hilbert space. By establishing a precise connection between optimal transport
metrics, optimal quantization, and learning theory, we derive new probabilistic
bounds for the per... | computer science |
5,275 | Robustness and Generalization for Metric Learning | cs.LG | Metric learning has attracted a lot of interest over the last decade, but the
generalization ability of such methods has not been thoroughly studied. In this
paper, we introduce an adaptation of the notion of algorithmic robustness
(previously introduced by Xu and Mannor) that can be used to derive
generalization bound... | computer science |
5,276 | Learning Manifolds with K-Means and K-Flats | cs.LG | We study the problem of estimating a manifold from random samples. In
particular, we consider piecewise constant and piecewise linear estimators
induced by k-means and k-flats, and analyze their performance. We extend
previous results for k-means in two separate directions. First, we provide new
results for k-means rec... | computer science |
5,277 | Multiclass Learning with Simplex Coding | stat.ML | In this paper we discuss a novel framework for multiclass learning, defined
by a suitable coding/decoding strategy, namely the simplex coding, that allows
to generalize to multiple classes a relaxation approach commonly used in binary
classification. In this framework, a relaxation error analysis can be developed
avoid... | computer science |
5,278 | On spatial selectivity and prediction across conditions with fMRI | stat.ML | Researchers in functional neuroimaging mostly use activation coordinates to
formulate their hypotheses. Instead, we propose to use the full statistical
images to define regions of interest (ROIs). This paper presents two machine
learning approaches, transfer learning and selection transfer, that are
compared upon their... | computer science |
5,279 | Rank Centrality: Ranking from Pair-wise Comparisons | cs.LG | The question of aggregating pair-wise comparisons to obtain a global ranking
over a collection of objects has been of interest for a very long time: be it
ranking of online gamers (e.g. MSR's TrueSkill system) and chess players,
aggregating social opinions, or deciding which product to sell based on
transactions. In mo... | computer science |
5,280 | Bandits with heavy tail | stat.ML | The stochastic multi-armed bandit problem is well understood when the reward
distributions are sub-Gaussian. In this paper we examine the bandit problem
under the weaker assumption that the distributions have moments of order
1+\epsilon, for some $\epsilon \in (0,1]$. Surprisingly, moments of order 2
(i.e., finite vari... | computer science |
5,281 | Fused Multiple Graphical Lasso | cs.LG | In this paper, we consider the problem of estimating multiple graphical
models simultaneously using the fused lasso penalty, which encourages adjacent
graphs to share similar structures. A motivating example is the analysis of
brain networks of Alzheimer's disease using neuroimaging data. Specifically, we
may wish to e... | computer science |
5,282 | Query Complexity of Derivative-Free Optimization | stat.ML | This paper provides lower bounds on the convergence rate of Derivative Free
Optimization (DFO) with noisy function evaluations, exposing a fundamental and
unavoidable gap between the performance of algorithms with access to gradients
and those with access to only function evaluations. However, there are
situations in w... | computer science |
5,283 | Minimax Multi-Task Learning and a Generalized Loss-Compositional
Paradigm for MTL | cs.LG | Since its inception, the modus operandi of multi-task learning (MTL) has been
to minimize the task-wise mean of the empirical risks. We introduce a
generalized loss-compositional paradigm for MTL that includes a spectrum of
formulations as a subfamily. One endpoint of this spectrum is minimax MTL: a
new MTL formulation... | computer science |
5,284 | Generalized Canonical Correlation Analysis for Disparate Data Fusion | stat.ML | Manifold matching works to identify embeddings of multiple disparate data
spaces into the same low-dimensional space, where joint inference can be
pursued. It is an enabling methodology for fusion and inference from multiple
and massive disparate data sources. In this paper we focus on a method called
Canonical Correla... | computer science |
5,285 | Efficient Regularized Least-Squares Algorithms for Conditional Ranking
on Relational Data | cs.LG | In domains like bioinformatics, information retrieval and social network
analysis, one can find learning tasks where the goal consists of inferring a
ranking of objects, conditioned on a particular target object. We present a
general kernel framework for learning conditional rankings from various types
of relational da... | computer science |
5,286 | A Bayesian Nonparametric Approach to Image Super-resolution | cs.LG | Super-resolution methods form high-resolution images from low-resolution
images. In this paper, we develop a new Bayesian nonparametric model for
super-resolution. Our method uses a beta-Bernoulli process to learn a set of
recurring visual patterns, called dictionary elements, from the data. Because
it is nonparametric... | computer science |
5,287 | Minimizing inter-subject variability in fNIRS based Brain Computer
Interfaces via multiple-kernel support vector learning | stat.ML | Brain signal variability in the measurements obtained from different subjects
during different sessions significantly deteriorates the accuracy of most
brain-computer interface (BCI) systems. Moreover these variabilities, also
known as inter-subject or inter-session variabilities, require lengthy
calibration sessions b... | computer science |
5,288 | Optimal Weighting of Multi-View Data with Low Dimensional Hidden States | stat.ML | In Natural Language Processing (NLP) tasks, data often has the following two
properties: First, data can be chopped into multi-views which has been
successfully used for dimension reduction purposes. For example, in topic
classification, every paper can be chopped into the title, the main text and
the references. Howev... | computer science |
5,289 | Subset Selection for Gaussian Markov Random Fields | cs.LG | Given a Gaussian Markov random field, we consider the problem of selecting a
subset of variables to observe which minimizes the total expected squared
prediction error of the unobserved variables. We first show that finding an
exact solution is NP-hard even for a restricted class of Gaussian Markov random
fields, calle... | computer science |
5,290 | Sparse Ising Models with Covariates | stat.ML | There has been a lot of work fitting Ising models to multivariate binary data
in order to understand the conditional dependency relationships between the
variables. However, additional covariates are frequently recorded together with
the binary data, and may influence the dependence relationships. Motivated by
such a d... | computer science |
5,291 | Bethe Bounds and Approximating the Global Optimum | cs.LG | Inference in general Markov random fields (MRFs) is NP-hard, though
identifying the maximum a posteriori (MAP) configuration of pairwise MRFs with
submodular cost functions is efficiently solvable using graph cuts. Marginal
inference, however, even for this restricted class, is in #P. We prove new
formulations of deriv... | computer science |
5,292 | Policy Evaluation with Variance Related Risk Criteria in Markov Decision
Processes | cs.LG | In this paper we extend temporal difference policy evaluation algorithms to
performance criteria that include the variance of the cumulative reward. Such
criteria are useful for risk management, and are important in domains such as
finance and process control. We propose both TD(0) and LSTD(lambda) variants
with linear... | computer science |
5,293 | Semi-Supervised Domain Adaptation with Non-Parametric Copulas | stat.ML | A new framework based on the theory of copulas is proposed to address semi-
supervised domain adaptation problems. The presented method factorizes any
multivariate density into a product of marginal distributions and bivariate
cop- ula functions. Therefore, changes in each of these factors can be detected
and corrected... | computer science |
5,294 | Follow the Leader If You Can, Hedge If You Must | cs.LG | Follow-the-Leader (FTL) is an intuitive sequential prediction strategy that
guarantees constant regret in the stochastic setting, but has terrible
performance for worst-case data. Other hedging strategies have better
worst-case guarantees but may perform much worse than FTL if the data are not
maximally adversarial. We... | computer science |
5,295 | Tree-dependent Component Analysis | cs.LG | We present a generalization of independent component analysis (ICA), where
instead of looking for a linear transform that makes the data components
independent, we look for a transform that makes the data components well fit by
a tree-structured graphical model. Treating the problem as a semiparametric
statistical prob... | computer science |
5,296 | Continuation Methods for Mixing Heterogenous Sources | cs.LG | A number of modern learning tasks involve estimation from heterogeneous
information sources. This includes classification with labeled and unlabeled
data as well as other problems with analogous structure such as competitive
(game theoretic) problems. The associated estimation problems can be typically
reduced to solvi... | computer science |
5,297 | An Information-Theoretic External Cluster-Validity Measure | cs.LG | In this paper we propose a measure of clustering quality or accuracy that is
appropriate in situations where it is desirable to evaluate a clustering
algorithm by somehow comparing the clusters it produces with ``ground truth'
consisting of classes assigned to the patterns by manual means or some other
means in whose v... | computer science |
5,298 | Dimension Correction for Hierarchical Latent Class Models | cs.LG | Model complexity is an important factor to consider when selecting among
graphical models. When all variables are observed, the complexity of a model
can be measured by its standard dimension, i.e. the number of independent
parameters. When hidden variables are present, however, standard dimension
might no longer be ap... | computer science |
5,299 | Almost-everywhere algorithmic stability and generalization error | cs.LG | We explore in some detail the notion of algorithmic stability as a viable
framework for analyzing the generalization error of learning algorithms. We
introduce the new notion of training stability of a learning algorithm and show
that, in a general setting, it is sufficient for good bounds on generalization
error. In t... | computer science |
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