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5,700 | Discovering causal structures in binary exclusive-or skew acyclic models | cs.LG | Discovering causal relations among observed variables in a given data set is
a main topic in studies of statistics and artificial intelligence. Recently,
some techniques to discover an identifiable causal structure have been explored
based on non-Gaussianity of the observed data distribution. However, most of
these are... | computer science |
5,701 | Detecting low-complexity unobserved causes | cs.LG | We describe a method that infers whether statistical dependences between two
observed variables X and Y are due to a "direct" causal link or only due to a
connecting causal path that contains an unobserved variable of low complexity,
e.g., a binary variable. This problem is motivated by statistical genetics.
Given a ge... | computer science |
5,702 | Asymptotic Efficiency of Deterministic Estimators for Discrete
Energy-Based Models: Ratio Matching and Pseudolikelihood | cs.LG | Standard maximum likelihood estimation cannot be applied to discrete
energy-based models in the general case because the computation of exact model
probabilities is intractable. Recent research has seen the proposal of several
new estimators designed specifically to overcome this intractability, but
virtually nothing i... | computer science |
5,703 | Reconstructing Pompeian Households | cs.LG | A database of objects discovered in houses in the Roman city of Pompeii
provides a unique view of ordinary life in an ancient city. Experts have used
this collection to study the structure of Roman households, exploring the
distribution and variability of tasks in architectural spaces, but such
approaches are necessari... | computer science |
5,704 | Conditional Restricted Boltzmann Machines for Structured Output
Prediction | cs.LG | Conditional Restricted Boltzmann Machines (CRBMs) are rich probabilistic
models that have recently been applied to a wide range of problems, including
collaborative filtering, classification, and modeling motion capture data.
While much progress has been made in training non-conditional RBMs, these
algorithms are not a... | computer science |
5,705 | Fractional Moments on Bandit Problems | cs.LG | Reinforcement learning addresses the dilemma between exploration to find
profitable actions and exploitation to act according to the best observations
already made. Bandit problems are one such class of problems in stateless
environments that represent this explore/exploit situation. We propose a
learning algorithm for... | computer science |
5,706 | Partial Order MCMC for Structure Discovery in Bayesian Networks | cs.LG | We present a new Markov chain Monte Carlo method for estimating posterior
probabilities of structural features in Bayesian networks. The method draws
samples from the posterior distribution of partial orders on the nodes; for
each sampled partial order, the conditional probabilities of interest are
computed exactly. We... | computer science |
5,707 | Identifiability of Causal Graphs using Functional Models | cs.LG | This work addresses the following question: Under what assumptions on the
data generating process can one infer the causal graph from the joint
distribution? The approach taken by conditional independence-based causal
discovery methods is based on two assumptions: the Markov condition and
faithfulness. It has been show... | computer science |
5,708 | Nonparametric Divergence Estimation with Applications to Machine
Learning on Distributions | cs.LG | Low-dimensional embedding, manifold learning, clustering, classification, and
anomaly detection are among the most important problems in machine learning.
The existing methods usually consider the case when each instance has a fixed,
finite-dimensional feature representation. Here we consider a different
setting. We as... | computer science |
5,709 | New Probabilistic Bounds on Eigenvalues and Eigenvectors of Random
Kernel Matrices | cs.LG | Kernel methods are successful approaches for different machine learning
problems. This success is mainly rooted in using feature maps and kernel
matrices. Some methods rely on the eigenvalues/eigenvectors of the kernel
matrix, while for other methods the spectral information can be used to
estimate the excess risk. An ... | computer science |
5,710 | An Efficient Algorithm for Computing Interventional Distributions in
Latent Variable Causal Models | cs.LG | Probabilistic inference in graphical models is the task of computing marginal
and conditional densities of interest from a factorized representation of a
joint probability distribution. Inference algorithms such as variable
elimination and belief propagation take advantage of constraints embedded in
this factorization ... | computer science |
5,711 | Robust learning Bayesian networks for prior belief | cs.LG | Recent reports have described that learning Bayesian networks are highly
sensitive to the chosen equivalent sample size (ESS) in the Bayesian Dirichlet
equivalence uniform (BDeu). This sensitivity often engenders some unstable or
undesirable results. This paper describes some asymptotic analyses of BDeu to
explain the ... | computer science |
5,712 | Sparse matrix-variate Gaussian process blockmodels for network modeling | cs.LG | We face network data from various sources, such as protein interactions and
online social networks. A critical problem is to model network interactions and
identify latent groups of network nodes. This problem is challenging due to
many reasons. For example, the network nodes are interdependent instead of
independent o... | computer science |
5,713 | Hierarchical Maximum Margin Learning for Multi-Class Classification | cs.LG | Due to myriads of classes, designing accurate and efficient classifiers
becomes very challenging for multi-class classification. Recent research has
shown that class structure learning can greatly facilitate multi-class
learning. In this paper, we propose a novel method to learn the class structure
for multi-class clas... | computer science |
5,714 | Tightening MRF Relaxations with Planar Subproblems | cs.LG | We describe a new technique for computing lower-bounds on the minimum energy
configuration of a planar Markov Random Field (MRF). Our method successively
adds large numbers of constraints and enforces consistency over binary
projections of the original problem state space. These constraints are
represented in terms of ... | computer science |
5,715 | Risk Bounds for Infinitely Divisible Distribution | stat.ML | In this paper, we study the risk bounds for samples independently drawn from
an infinitely divisible (ID) distribution. In particular, based on a martingale
method, we develop two deviation inequalities for a sequence of random
variables of an ID distribution with zero Gaussian component. By applying the
deviation ineq... | computer science |
5,716 | Kernel-based Conditional Independence Test and Application in Causal
Discovery | cs.LG | Conditional independence testing is an important problem, especially in
Bayesian network learning and causal discovery. Due to the curse of
dimensionality, testing for conditional independence of continuous variables is
particularly challenging. We propose a Kernel-based Conditional Independence
test (KCI-test), by con... | computer science |
5,717 | Smoothing Multivariate Performance Measures | cs.LG | A Support Vector Method for multivariate performance measures was recently
introduced by Joachims (2005). The underlying optimization problem is currently
solved using cutting plane methods such as SVM-Perf and BMRM. One can show that
these algorithms converge to an eta accurate solution in O(1/Lambda*e)
iterations, wh... | computer science |
5,718 | Sparse Topical Coding | cs.LG | We present sparse topical coding (STC), a non-probabilistic formulation of
topic models for discovering latent representations of large collections of
data. Unlike probabilistic topic models, STC relaxes the normalization
constraint of admixture proportions and the constraint of defining a normalized
likelihood functio... | computer science |
5,719 | Testing whether linear equations are causal: A free probability theory
approach | cs.LG | We propose a method that infers whether linear relations between two
high-dimensional variables X and Y are due to a causal influence from X to Y or
from Y to X. The earlier proposed so-called Trace Method is extended to the
regime where the dimension of the observed variables exceeds the sample size.
Based on previous... | computer science |
5,720 | On the Sample Complexity of Predictive Sparse Coding | cs.LG | The goal of predictive sparse coding is to learn a representation of examples
as sparse linear combinations of elements from a dictionary, such that a
learned hypothesis linear in the new representation performs well on a
predictive task. Predictive sparse coding algorithms recently have demonstrated
impressive perform... | computer science |
5,721 | Classification approach based on association rules mining for unbalanced
data | stat.ML | This paper deals with the binary classification task when the target class
has the lower probability of occurrence. In such situation, it is not possible
to build a powerful classifier by using standard methods such as logistic
regression, classification tree, discriminant analysis, etc. To overcome this
short-coming o... | computer science |
5,722 | Clustering using Max-norm Constrained Optimization | cs.LG | We suggest using the max-norm as a convex surrogate constraint for
clustering. We show how this yields a better exact cluster recovery guarantee
than previously suggested nuclear-norm relaxation, and study the effectiveness
of our method, and other related convex relaxations, compared to other
clustering approaches. | computer science |
5,723 | Training Restricted Boltzmann Machines on Word Observations | cs.LG | The restricted Boltzmann machine (RBM) is a flexible tool for modeling
complex data, however there have been significant computational difficulties in
using RBMs to model high-dimensional multinomial observations. In natural
language processing applications, words are naturally modeled by K-ary discrete
distributions, ... | computer science |
5,724 | Efficiently Sampling Multiplicative Attribute Graphs Using a
Ball-Dropping Process | stat.ML | We introduce a novel and efficient sampling algorithm for the Multiplicative
Attribute Graph Model (MAGM - Kim and Leskovec (2010)}). Our algorithm is
\emph{strictly} more efficient than the algorithm proposed by Yun and
Vishwanathan (2012), in the sense that our method extends the \emph{best} time
complexity guarantee... | computer science |
5,725 | Protocols for Learning Classifiers on Distributed Data | stat.ML | We consider the problem of learning classifiers for labeled data that has
been distributed across several nodes. Our goal is to find a single classifier,
with small approximation error, across all datasets while minimizing the
communication between nodes. This setting models real-world communication
bottlenecks in the ... | computer science |
5,726 | PAC-Bayesian Generalization Bound on Confusion Matrix for Multi-Class
Classification | stat.ML | In this work, we propose a PAC-Bayes bound for the generalization risk of the
Gibbs classifier in the multi-class classification framework. The novelty of
our work is the critical use of the confusion matrix of a classifier as an
error measure; this puts our contribution in the line of work aiming at dealing
with perfo... | computer science |
5,727 | Learning from Distributions via Support Measure Machines | stat.ML | This paper presents a kernel-based discriminative learning framework on
probability measures. Rather than relying on large collections of vectorial
training examples, our framework learns using a collection of probability
distributions that have been constructed to meaningfully represent training
data. By representing ... | computer science |
5,728 | Sparse Trace Norm Regularization | cs.LG | We study the problem of estimating multiple predictive functions from a
dictionary of basis functions in the nonparametric regression setting. Our
estimation scheme assumes that each predictive function can be estimated in the
form of a linear combination of the basis functions. By assuming that the
coefficient matrix ... | computer science |
5,729 | Bayesian Structure Learning for Markov Random Fields with a Spike and
Slab Prior | stat.ML | In recent years a number of methods have been developed for automatically
learning the (sparse) connectivity structure of Markov Random Fields. These
methods are mostly based on L1-regularized optimization which has a number of
disadvantages such as the inability to assess model uncertainty and expensive
cross-validati... | computer science |
5,730 | No More Pesky Learning Rates | stat.ML | The performance of stochastic gradient descent (SGD) depends critically on
how learning rates are tuned and decreased over time. We propose a method to
automatically adjust multiple learning rates so as to minimize the expected
error at any one time. The method relies on local gradient variations across
samples. In our... | computer science |
5,731 | A New Greedy Algorithm for Multiple Sparse Regression | stat.ML | This paper proposes a new algorithm for multiple sparse regression in high
dimensions, where the task is to estimate the support and values of several
(typically related) sparse vectors from a few noisy linear measurements. Our
algorithm is a "forward-backward" greedy procedure that -- uniquely -- operates
on two disti... | computer science |
5,732 | Sparse projections onto the simplex | cs.LG | Most learning methods with rank or sparsity constraints use convex
relaxations, which lead to optimization with the nuclear norm or the
$\ell_1$-norm. However, several important learning applications cannot benefit
from this approach as they feature these convex norms as constraints in
addition to the non-convex rank a... | computer science |
5,733 | Fast Cross-Validation via Sequential Testing | cs.LG | With the increasing size of today's data sets, finding the right parameter
configuration in model selection via cross-validation can be an extremely
time-consuming task. In this paper we propose an improved cross-validation
procedure which uses nonparametric testing coupled with sequential analysis to
determine the bes... | computer science |
5,734 | Practical Bayesian Optimization of Machine Learning Algorithms | stat.ML | Machine learning algorithms frequently require careful tuning of model
hyperparameters, regularization terms, and optimization parameters.
Unfortunately, this tuning is often a "black art" that requires expert
experience, unwritten rules of thumb, or sometimes brute-force search. Much
more appealing is the idea of deve... | computer science |
5,735 | Statistical Consistency of Finite-dimensional Unregularized Linear
Classification | cs.LG | This manuscript studies statistical properties of linear classifiers obtained
through minimization of an unregularized convex risk over a finite sample.
Although the results are explicitly finite-dimensional, inputs may be passed
through feature maps; in this way, in addition to treating the consistency of
logistic reg... | computer science |
5,736 | Identifiability and Unmixing of Latent Parse Trees | stat.ML | This paper explores unsupervised learning of parsing models along two
directions. First, which models are identifiable from infinite data? We use a
general technique for numerically checking identifiability based on the rank of
a Jacobian matrix, and apply it to several standard constituency and dependency
parsing mode... | computer science |
5,737 | CORL: A Continuous-state Offset-dynamics Reinforcement Learner | cs.LG | Continuous state spaces and stochastic, switching dynamics characterize a
number of rich, realworld domains, such as robot navigation across varying
terrain. We describe a reinforcementlearning algorithm for learning in these
domains and prove for certain environments the algorithm is probably
approximately correct wit... | computer science |
5,738 | Greedy Block Coordinate Descent for Large Scale Gaussian Process
Regression | cs.LG | We propose a variable decomposition algorithm -greedy block coordinate
descent (GBCD)- in order to make dense Gaussian process regression practical
for large scale problems. GBCD breaks a large scale optimization into a series
of small sub-problems. The challenge in variable decomposition algorithms is
the identificati... | computer science |
5,739 | Approximating the Partition Function by Deleting and then Correcting for
Model Edges | cs.LG | We propose an approach for approximating the partition function which is
based on two steps: (1) computing the partition function of a simplified model
which is obtained by deleting model edges, and (2) rectifying the result by
applying an edge-by-edge correction. The approach leads to an intuitive
framework in which o... | computer science |
5,740 | Multi-View Learning in the Presence of View Disagreement | cs.LG | Traditional multi-view learning approaches suffer in the presence of view
disagreement,i.e., when samples in each view do not belong to the same class
due to view corruption, occlusion or other noise processes. In this paper we
present a multi-view learning approach that uses a conditional entropy
criterion to detect v... | computer science |
5,741 | Bounds on the Bethe Free Energy for Gaussian Networks | cs.LG | We address the problem of computing approximate marginals in Gaussian
probabilistic models by using mean field and fractional Bethe approximations.
As an extension of Welling and Teh (2001), we define the Gaussian fractional
Bethe free energy in terms of the moment parameters of the approximate
marginals and derive an ... | computer science |
5,742 | Learning Convex Inference of Marginals | cs.LG | Graphical models trained using maximum likelihood are a common tool for
probabilistic inference of marginal distributions. However, this approach
suffers difficulties when either the inference process or the model is
approximate. In this paper, the inference process is first defined to be the
minimization of a convex f... | computer science |
5,743 | Projected Subgradient Methods for Learning Sparse Gaussians | cs.LG | Gaussian Markov random fields (GMRFs) are useful in a broad range of
applications. In this paper we tackle the problem of learning a sparse GMRF in
a high-dimensional space. Our approach uses the l1-norm as a regularization on
the inverse covariance matrix. We utilize a novel projected gradient method,
which is faster ... | computer science |
5,744 | Convex Point Estimation using Undirected Bayesian Transfer Hierarchies | cs.LG | When related learning tasks are naturally arranged in a hierarchy, an
appealing approach for coping with scarcity of instances is that of transfer
learning using a hierarchical Bayes framework. As fully Bayesian computations
can be difficult and computationally demanding, it is often desirable to use
posterior point es... | computer science |
5,745 | Multi-View Learning over Structured and Non-Identical Outputs | cs.LG | In many machine learning problems, labeled training data is limited but
unlabeled data is ample. Some of these problems have instances that can be
factored into multiple views, each of which is nearly sufficent in determining
the correct labels. In this paper we present a new algorithm for probabilistic
multi-view lear... | computer science |
5,746 | Constrained Approximate Maximum Entropy Learning of Markov Random Fields | cs.LG | Parameter estimation in Markov random fields (MRFs) is a difficult task, in
which inference over the network is run in the inner loop of a gradient descent
procedure. Replacing exact inference with approximate methods such as loopy
belief propagation (LBP) can suffer from poor convergence. In this paper, we
provide a d... | computer science |
5,747 | Cumulative distribution networks and the derivative-sum-product
algorithm | cs.LG | We introduce a new type of graphical model called a "cumulative distribution
network" (CDN), which expresses a joint cumulative distribution as a product of
local functions. Each local function can be viewed as providing evidence about
possible orderings, or rankings, of variables. Interestingly, we find that the
condi... | computer science |
5,748 | Convergent Message-Passing Algorithms for Inference over General Graphs
with Convex Free Energies | cs.LG | Inference problems in graphical models can be represented as a constrained
optimization of a free energy function. It is known that when the Bethe free
energy is used, the fixedpoints of the belief propagation (BP) algorithm
correspond to the local minima of the free energy. However BP fails to converge
in many cases o... | computer science |
5,749 | Bayesian Out-Trees | cs.LG | A Bayesian treatment of latent directed graph structure for non-iid data is
provided where each child datum is sampled with a directed conditional
dependence on a single unknown parent datum. The latent graph structure is
assumed to lie in the family of directed out-tree graphs which leads to
efficient Bayesian inferen... | computer science |
5,750 | Estimation and Clustering with Infinite Rankings | cs.LG | This paper presents a natural extension of stagewise ranking to the the case
of infinitely many items. We introduce the infinite generalized Mallows model
(IGM), describe its properties and give procedures to estimate it from data.
For estimation of multimodal distributions we introduce the
Exponential-Blurring-Mean-Sh... | computer science |
5,751 | Small Sample Inference for Generalization Error in Classification Using
the CUD Bound | cs.LG | Confidence measures for the generalization error are crucial when small
training samples are used to construct classifiers. A common approach is to
estimate the generalization error by resampling and then assume the resampled
estimator follows a known distribution to form a confidence set [Kohavi 1995,
Martin 1996,Yang... | computer science |
5,752 | The Phylogenetic Indian Buffet Process: A Non-Exchangeable Nonparametric
Prior for Latent Features | cs.LG | Nonparametric Bayesian models are often based on the assumption that the
objects being modeled are exchangeable. While appropriate in some applications
(e.g., bag-of-words models for documents), exchangeability is sometimes assumed
simply for computational reasons; non-exchangeable models might be a better
choice for a... | computer science |
5,753 | Modelling local and global phenomena with sparse Gaussian processes | cs.LG | Much recent work has concerned sparse approximations to speed up the Gaussian
process regression from the unfavorable O(n3) scaling in computational time to
O(nm2). Thus far, work has concentrated on models with one covariance function.
However, in many practical situations additive models with multiple covariance
func... | computer science |
5,754 | Flexible Priors for Exemplar-based Clustering | cs.LG | Exemplar-based clustering methods have been shown to produce state-of-the-art
results on a number of synthetic and real-world clustering problems. They are
appealing because they offer computational benefits over latent-mean models and
can handle arbitrary pairwise similarity measures between data points. However,
when... | computer science |
5,755 | Hybrid Variational/Gibbs Collapsed Inference in Topic Models | cs.LG | Variational Bayesian inference and (collapsed) Gibbs sampling are the two
important classes of inference algorithms for Bayesian networks. Both have
their advantages and disadvantages: collapsed Gibbs sampling is unbiased but is
also inefficient for large count values and requires averaging over many
samples to reduce ... | computer science |
5,756 | Constraint-free Graphical Model with Fast Learning Algorithm | cs.LG | In this paper, we propose a simple, versatile model for learning the
structure and parameters of multivariate distributions from a data set.
Learning a Markov network from a given data set is not a simple problem,
because Markov networks rigorously represent Markov properties, and this rigor
imposes complex constraints... | computer science |
5,757 | DANCo: Dimensionality from Angle and Norm Concentration | cs.LG | In the last decades the estimation of the intrinsic dimensionality of a
dataset has gained considerable importance. Despite the great deal of research
work devoted to this task, most of the proposed solutions prove to be
unreliable when the intrinsic dimensionality of the input dataset is high and
the manifold where th... | computer science |
5,758 | Residual Component Analysis: Generalising PCA for more flexible
inference in linear-Gaussian models | cs.LG | Probabilistic principal component analysis (PPCA) seeks a low dimensional
representation of a data set in the presence of independent spherical Gaussian
noise. The maximum likelihood solution for the model is an eigenvalue problem
on the sample covariance matrix. In this paper we consider the situation where
the data v... | computer science |
5,759 | A Unified Robust Classification Model | cs.LG | A wide variety of machine learning algorithms such as support vector machine
(SVM), minimax probability machine (MPM), and Fisher discriminant analysis
(FDA), exist for binary classification. The purpose of this paper is to provide
a unified classification model that includes the above models through a robust
optimizat... | computer science |
5,760 | Bayesian Nonexhaustive Learning for Online Discovery and Modeling of
Emerging Classes | cs.LG | We present a framework for online inference in the presence of a
nonexhaustively defined set of classes that incorporates supervised
classification with class discovery and modeling. A Dirichlet process prior
(DPP) model defined over class distributions ensures that both known and
unknown class distributions originate ... | computer science |
5,761 | Convex Multitask Learning with Flexible Task Clusters | cs.LG | Traditionally, multitask learning (MTL) assumes that all the tasks are
related. This can lead to negative transfer when tasks are indeed incoherent.
Recently, a number of approaches have been proposed that alleviate this problem
by discovering the underlying task clusters or relationships. However, they are
limited to ... | computer science |
5,762 | Distributed Tree Kernels | cs.LG | In this paper, we propose the distributed tree kernels (DTK) as a novel
method to reduce time and space complexity of tree kernels. Using a linear
complexity algorithm to compute vectors for trees, we embed feature spaces of
tree fragments in low-dimensional spaces where the kernel computation is
directly done with dot... | computer science |
5,763 | A Convex Feature Learning Formulation for Latent Task Structure
Discovery | cs.LG | This paper considers the multi-task learning problem and in the setting where
some relevant features could be shared across few related tasks. Most of the
existing methods assume the extent to which the given tasks are related or
share a common feature space to be known apriori. In real-world applications
however, it i... | computer science |
5,764 | Information-theoretic Semi-supervised Metric Learning via Entropy
Regularization | cs.LG | We propose a general information-theoretic approach called Seraph
(SEmi-supervised metRic leArning Paradigm with Hyper-sparsity) for metric
learning that does not rely upon the manifold assumption. Given the probability
parameterized by a Mahalanobis distance, we maximize the entropy of that
probability on labeled data... | computer science |
5,765 | Compact Hyperplane Hashing with Bilinear Functions | cs.LG | Hyperplane hashing aims at rapidly searching nearest points to a hyperplane,
and has shown practical impact in scaling up active learning with SVMs.
Unfortunately, the existing randomized methods need long hash codes to achieve
reasonable search accuracy and thus suffer from reduced search speed and large
memory overhe... | computer science |
5,766 | Improved Information Gain Estimates for Decision Tree Induction | cs.LG | Ensembles of classification and regression trees remain popular machine
learning methods because they define flexible non-parametric models that
predict well and are computationally efficient both during training and
testing. During induction of decision trees one aims to find predicates that
are maximally informative ... | computer science |
5,767 | On the Size of the Online Kernel Sparsification Dictionary | cs.LG | We analyze the size of the dictionary constructed from online kernel
sparsification, using a novel formula that expresses the expected determinant
of the kernel Gram matrix in terms of the eigenvalues of the covariance
operator. Using this formula, we are able to connect the cardinality of the
dictionary with the eigen... | computer science |
5,768 | Robust Multiple Manifolds Structure Learning | cs.LG | We present a robust multiple manifolds structure learning (RMMSL) scheme to
robustly estimate data structures under the multiple low intrinsic dimensional
manifolds assumption. In the local learning stage, RMMSL efficiently estimates
local tangent space by weighted low-rank matrix factorization. In the global
learning ... | computer science |
5,769 | Convergence Rates of Biased Stochastic Optimization for Learning Sparse
Ising Models | cs.LG | We study the convergence rate of stochastic optimization of exact (NP-hard)
objectives, for which only biased estimates of the gradient are available. We
motivate this problem in the context of learning the structure and parameters
of Ising models. We first provide a convergence-rate analysis of deterministic
errors fo... | computer science |
5,770 | Robust PCA in High-dimension: A Deterministic Approach | cs.LG | We consider principal component analysis for contaminated data-set in the
high dimensional regime, where the dimensionality of each observation is
comparable or even more than the number of observations. We propose a
deterministic high-dimensional robust PCA algorithm which inherits all
theoretical properties of its ra... | computer science |
5,771 | Fast Bounded Online Gradient Descent Algorithms for Scalable
Kernel-Based Online Learning | cs.LG | Kernel-based online learning has often shown state-of-the-art performance for
many online learning tasks. It, however, suffers from a major shortcoming, that
is, the unbounded number of support vectors, making it non-scalable and
unsuitable for applications with large-scale datasets. In this work, we study
the problem ... | computer science |
5,772 | Deep Mixtures of Factor Analysers | cs.LG | An efficient way to learn deep density models that have many layers of latent
variables is to learn one layer at a time using a model that has only one layer
of latent variables. After learning each layer, samples from the posterior
distributions for that layer are used as training data for learning the next
layer. Thi... | computer science |
5,773 | Efficient Euclidean Projections onto the Intersection of Norm Balls | cs.LG | Using sparse-inducing norms to learn robust models has received increasing
attention from many fields for its attractive properties. Projection-based
methods have been widely applied to learning tasks constrained by such norms.
As a key building block of these methods, an efficient operator for Euclidean
projection ont... | computer science |
5,774 | Groupwise Constrained Reconstruction for Subspace Clustering | cs.LG | Reconstruction based subspace clustering methods compute a self
reconstruction matrix over the samples and use it for spectral clustering to
obtain the final clustering result. Their success largely relies on the
assumption that the underlying subspaces are independent, which, however, does
not always hold in the appli... | computer science |
5,775 | Partial-Hessian Strategies for Fast Learning of Nonlinear Embeddings | cs.LG | Stochastic neighbor embedding (SNE) and related nonlinear manifold learning
algorithms achieve high-quality low-dimensional representations of similarity
data, but are notoriously slow to train. We propose a generic formulation of
embedding algorithms that includes SNE and other existing algorithms, and study
their rel... | computer science |
5,776 | Analysis of Kernel Mean Matching under Covariate Shift | cs.LG | In real supervised learning scenarios, it is not uncommon that the training
and test sample follow different probability distributions, thus rendering the
necessity to correct the sampling bias. Focusing on a particular covariate
shift problem, we derive high probability confidence bounds for the kernel mean
matching (... | computer science |
5,777 | Dirichlet Process with Mixed Random Measures: A Nonparametric Topic
Model for Labeled Data | cs.LG | We describe a nonparametric topic model for labeled data. The model uses a
mixture of random measures (MRM) as a base distribution of the Dirichlet
process (DP) of the HDP framework, so we call it the DP-MRM. To model labeled
data, we define a DP distributed random measure for each label, and the
resulting model genera... | computer science |
5,778 | Max-Margin Nonparametric Latent Feature Models for Link Prediction | cs.LG | We present a max-margin nonparametric latent feature model, which unites the
ideas of max-margin learning and Bayesian nonparametrics to discover
discriminative latent features for link prediction and automatically infer the
unknown latent social dimension. By minimizing a hinge-loss using the linear
expectation operat... | computer science |
5,779 | Predicting accurate probabilities with a ranking loss | cs.LG | In many real-world applications of machine learning classifiers, it is
essential to predict the probability of an example belonging to a particular
class. This paper proposes a simple technique for predicting probabilities
based on optimizing a ranking loss, followed by isotonic regression. This
semi-parametric techniq... | computer science |
5,780 | The Convexity and Design of Composite Multiclass Losses | cs.LG | We consider composite loss functions for multiclass prediction comprising a
proper (i.e., Fisher-consistent) loss over probability distributions and an
inverse link function. We establish conditions for their (strong) convexity and
explore the implications. We also show how the separation of concerns afforded
by using ... | computer science |
5,781 | Tighter Variational Representations of f-Divergences via Restriction to
Probability Measures | cs.LG | We show that the variational representations for f-divergences currently used
in the literature can be tightened. This has implications to a number of
methods recently proposed based on this representation. As an example
application we use our tighter representation to derive a general f-divergence
estimator based on t... | computer science |
5,782 | Nonparametric variational inference | cs.LG | Variational methods are widely used for approximate posterior inference.
However, their use is typically limited to families of distributions that enjoy
particular conjugacy properties. To circumvent this limitation, we propose a
family of variational approximations inspired by nonparametric kernel density
estimation. ... | computer science |
5,783 | Sparse Additive Functional and Kernel CCA | cs.LG | Canonical Correlation Analysis (CCA) is a classical tool for finding
correlations among the components of two random vectors. In recent years, CCA
has been widely applied to the analysis of genomic data, where it is common for
researchers to perform multiple assays on a single set of patient samples.
Recent work has pr... | computer science |
5,784 | Dependent Hierarchical Normalized Random Measures for Dynamic Topic
Modeling | cs.LG | We develop dependent hierarchical normalized random measures and apply them
to dynamic topic modeling. The dependency arises via superposition, subsampling
and point transition on the underlying Poisson processes of these measures. The
measures used include normalised generalised Gamma processes that demonstrate
power ... | computer science |
5,785 | Efficient Active Algorithms for Hierarchical Clustering | cs.LG | Advances in sensing technologies and the growth of the internet have resulted
in an explosion in the size of modern datasets, while storage and processing
power continue to lag behind. This motivates the need for algorithms that are
efficient, both in terms of the number of measurements needed and running time.
To comb... | computer science |
5,786 | Group Sparse Additive Models | cs.LG | We consider the problem of sparse variable selection in nonparametric
additive models, with the prior knowledge of the structure among the covariates
to encourage those variables within a group to be selected jointly. Previous
works either study the group sparsity in the parametric setting (e.g., group
lasso), or addre... | computer science |
5,787 | Semi-Supervised Learning of Class Balance under Class-Prior Change by
Distribution Matching | cs.LG | In real-world classification problems, the class balance in the training
dataset does not necessarily reflect that of the test dataset, which can cause
significant estimation bias. If the class ratio of the test dataset is known,
instance re-weighting or resampling allows systematical bias correction.
However, learning... | computer science |
5,788 | Linear Regression with Limited Observation | cs.LG | We consider the most common variants of linear regression, including Ridge,
Lasso and Support-vector regression, in a setting where the learner is allowed
to observe only a fixed number of attributes of each example at training time.
We present simple and efficient algorithms for these problems: for Lasso and
Ridge reg... | computer science |
5,789 | Factorized Asymptotic Bayesian Hidden Markov Models | cs.LG | This paper addresses the issue of model selection for hidden Markov models
(HMMs). We generalize factorized asymptotic Bayesian inference (FAB), which has
been recently developed for model selection on independent hidden variables
(i.e., mixture models), for time-dependent hidden variables. As with FAB in
mixture model... | computer science |
5,790 | LPQP for MAP: Putting LP Solvers to Better Use | cs.LG | MAP inference for general energy functions remains a challenging problem.
While most efforts are channeled towards improving the linear programming (LP)
based relaxation, this work is motivated by the quadratic programming (QP)
relaxation. We propose a novel MAP relaxation that penalizes the
Kullback-Leibler divergence... | computer science |
5,791 | Discriminative Probabilistic Prototype Learning | cs.LG | In this paper we propose a simple yet powerful method for learning
representations in supervised learning scenarios where each original input
datapoint is described by a set of vectors and their associated outputs may be
given by soft labels indicating, for example, class probabilities. We represent
an input datapoint ... | computer science |
5,792 | Estimating Densities with Non-Parametric Exponential Families | stat.ML | We propose a novel approach for density estimation with exponential families
for the case when the true density may not fall within the chosen family. Our
approach augments the sufficient statistics with features designed to
accumulate probability mass in the neighborhood of the observed points,
resulting in a non-para... | computer science |
5,793 | Fast Variational Inference in the Conjugate Exponential Family | cs.LG | We present a general method for deriving collapsed variational inference
algo- rithms for probabilistic models in the conjugate exponential family. Our
method unifies many existing approaches to collapsed variational inference. Our
collapsed variational inference leads to a new lower bound on the marginal
likelihood. W... | computer science |
5,794 | Analysis of Semi-Supervised Learning with the Yarowsky Algorithm | cs.LG | The Yarowsky algorithm is a rule-based semi-supervised learning algorithm
that has been successfully applied to some problems in computational
linguistics. The algorithm was not mathematically well understood until (Abney
2004) which analyzed some specific variants of the algorithm, and also proposed
some new algorithm... | computer science |
5,795 | Shift-Invariance Sparse Coding for Audio Classification | cs.LG | Sparse coding is an unsupervised learning algorithm that learns a succinct
high-level representation of the inputs given only unlabeled data; it
represents each input as a sparse linear combination of a set of basis
functions. Originally applied to modeling the human visual cortex, sparse
coding has also been shown to ... | computer science |
5,796 | Convergent Propagation Algorithms via Oriented Trees | cs.LG | Inference problems in graphical models are often approximated by casting them
as constrained optimization problems. Message passing algorithms, such as
belief propagation, have previously been suggested as methods for solving these
optimization problems. However, there are few convergence guarantees for such
algorithms... | computer science |
5,797 | Bayesian structure learning using dynamic programming and MCMC | cs.LG | MCMC methods for sampling from the space of DAGs can mix poorly due to the
local nature of the proposals that are commonly used. It has been shown that
sampling from the space of node orders yields better results [FK03, EW06].
Recently, Koivisto and Sood showed how one can analytically marginalize over
orders using dyn... | computer science |
5,798 | Apprenticeship Learning using Inverse Reinforcement Learning and
Gradient Methods | cs.LG | In this paper we propose a novel gradient algorithm to learn a policy from an
expert's observed behavior assuming that the expert behaves optimally with
respect to some unknown reward function of a Markovian Decision Problem. The
algorithm's aim is to find a reward function such that the resulting optimal
policy matche... | computer science |
5,799 | On Discarding, Caching, and Recalling Samples in Active Learning | cs.LG | We address challenges of active learning under scarce informational resources
in non-stationary environments. In real-world settings, data labeled and
integrated into a predictive model may become invalid over time. However, the
data can become informative again with switches in context and such changes may
indicate un... | computer science |
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