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5,800 | Learning Selectively Conditioned Forest Structures with Applications to
DBNs and Classification | cs.LG | Dealing with uncertainty in Bayesian Network structures using maximum a
posteriori (MAP) estimation or Bayesian Model Averaging (BMA) is often
intractable due to the superexponential number of possible directed, acyclic
graphs. When the prior is decomposable, two classes of graphs where efficient
learning can take plac... | computer science |
5,801 | Bayesian Active Distance Metric Learning | cs.LG | Distance metric learning is an important component for many tasks, such as
statistical classification and content-based image retrieval. Existing
approaches for learning distance metrics from pairwise constraints typically
suffer from two major problems. First, most algorithms only offer point
estimation of the distanc... | computer science |
5,802 | On Sensitivity of the MAP Bayesian Network Structure to the Equivalent
Sample Size Parameter | cs.LG | BDeu marginal likelihood score is a popular model selection criterion for
selecting a Bayesian network structure based on sample data. This
non-informative scoring criterion assigns same score for network structures
that encode same independence statements. However, before applying the BDeu
score, one must determine a ... | computer science |
5,803 | A Geometric Algorithm for Scalable Multiple Kernel Learning | cs.LG | We present a geometric formulation of the Multiple Kernel Learning (MKL)
problem. To do so, we reinterpret the problem of learning kernel weights as
searching for a kernel that maximizes the minimum (kernel) distance between two
convex polytopes. This interpretation combined with novel structural insights
from our geom... | computer science |
5,804 | Learning mixtures of spherical Gaussians: moment methods and spectral
decompositions | cs.LG | This work provides a computationally efficient and statistically consistent
moment-based estimator for mixtures of spherical Gaussians. Under the condition
that component means are in general position, a simple spectral decomposition
technique yields consistent parameter estimates from low-order observable
moments, wit... | computer science |
5,805 | Transductive Classification Methods for Mixed Graphs | cs.LG | In this paper we provide a principled approach to solve a transductive
classification problem involving a similar graph (edges tend to connect nodes
with same labels) and a dissimilar graph (edges tend to connect nodes with
opposing labels). Most of the existing methods, e.g., Information
Regularization (IR), Weighted ... | computer science |
5,806 | An Additive Model View to Sparse Gaussian Process Classifier Design | cs.LG | We consider the problem of designing a sparse Gaussian process classifier
(SGPC) that generalizes well. Viewing SGPC design as constructing an additive
model like in boosting, we present an efficient and effective SGPC design
method to perform a stage-wise optimization of a predictive loss function. We
introduce new me... | computer science |
5,807 | Predictive Approaches For Gaussian Process Classifier Model Selection | cs.LG | In this paper we consider the problem of Gaussian process classifier (GPC)
model selection with different Leave-One-Out (LOO) Cross Validation (CV) based
optimization criteria and provide a practical algorithm using LOO predictive
distributions with such criteria to select hyperparameters. Apart from the
standard avera... | computer science |
5,808 | Learning Markov Network Structure using Brownian Distance Covariance | stat.ML | In this paper, we present a simple non-parametric method for learning the
structure of undirected graphs from data that drawn from an underlying unknown
distribution. We propose to use Brownian distance covariance to estimate the
conditional independences between the random variables and encodes pairwise
Markov graph. ... | computer science |
5,809 | Shortest path distance in random k-nearest neighbor graphs | cs.LG | Consider a weighted or unweighted k-nearest neighbor graph that has been
built on n data points drawn randomly according to some density p on R^d. We
study the convergence of the shortest path distance in such graphs as the
sample size tends to infinity. We prove that for unweighted kNN graphs, this
distance converges ... | computer science |
5,810 | High-Dimensional Covariance Decomposition into Sparse Markov and
Independence Domains | cs.LG | In this paper, we present a novel framework incorporating a combination of
sparse models in different domains. We posit the observed data as generated
from a linear combination of a sparse Gaussian Markov model (with a sparse
precision matrix) and a sparse Gaussian independence model (with a sparse
covariance matrix). ... | computer science |
5,811 | Feature Selection via Probabilistic Outputs | cs.LG | This paper investigates two feature-scoring criteria that make use of
estimated class probabilities: one method proposed by \citet{shen} and a
complementary approach proposed below. We develop a theoretical framework to
analyze each criterion and show that both estimate the spread (across all
values of a given feature)... | computer science |
5,812 | Efficient and Practical Stochastic Subgradient Descent for Nuclear Norm
Regularization | cs.LG | We describe novel subgradient methods for a broad class of matrix
optimization problems involving nuclear norm regularization. Unlike existing
approaches, our method executes very cheap iterations by combining low-rank
stochastic subgradients with efficient incremental SVD updates, made possible
by highly optimized and... | computer science |
5,813 | Fast classification using sparse decision DAGs | cs.LG | In this paper we propose an algorithm that builds sparse decision DAGs
(directed acyclic graphs) from a list of base classifiers provided by an
external learning method such as AdaBoost. The basic idea is to cast the DAG
design task as a Markov decision process. Each instance can decide to use or to
skip each base clas... | computer science |
5,814 | Local Loss Optimization in Operator Models: A New Insight into Spectral
Learning | cs.LG | This paper re-visits the spectral method for learning latent variable models
defined in terms of observable operators. We give a new perspective on the
method, showing that operators can be recovered by minimizing a loss defined on
a finite subset of the domain. A non-convex optimization similar to the
spectral method ... | computer science |
5,815 | Joint Optimization and Variable Selection of High-dimensional Gaussian
Processes | cs.LG | Maximizing high-dimensional, non-convex functions through noisy observations
is a notoriously hard problem, but one that arises in many applications. In
this paper, we tackle this challenge by modeling the unknown function as a
sample from a high-dimensional Gaussian process (GP) distribution. Assuming
that the unknown... | computer science |
5,816 | Communications Inspired Linear Discriminant Analysis | cs.LG | We study the problem of supervised linear dimensionality reduction, taking an
information-theoretic viewpoint. The linear projection matrix is designed by
maximizing the mutual information between the projected signal and the class
label (based on a Shannon entropy measure). By harnessing a recent theoretical
result on... | computer science |
5,817 | Learning Parameterized Skills | cs.LG | We introduce a method for constructing skills capable of solving tasks drawn
from a distribution of parameterized reinforcement learning problems. The
method draws example tasks from a distribution of interest and uses the
corresponding learned policies to estimate the topology of the
lower-dimensional piecewise-smooth... | computer science |
5,818 | Online Bandit Learning against an Adaptive Adversary: from Regret to
Policy Regret | cs.LG | Online learning algorithms are designed to learn even when their input is
generated by an adversary. The widely-accepted formal definition of an online
algorithm's ability to learn is the game-theoretic notion of regret. We argue
that the standard definition of regret becomes inadequate if the adversary is
allowed to a... | computer science |
5,819 | Consistent Multilabel Ranking through Univariate Losses | cs.LG | We consider the problem of rank loss minimization in the setting of
multilabel classification, which is usually tackled by means of convex
surrogate losses defined on pairs of labels. Very recently, this approach was
put into question by a negative result showing that commonly used pairwise
surrogate losses, such as ex... | computer science |
5,820 | Parallelizing Exploration-Exploitation Tradeoffs with Gaussian Process
Bandit Optimization | cs.LG | Can one parallelize complex exploration exploitation tradeoffs? As an
example, consider the problem of optimal high-throughput experimental design,
where we wish to sequentially design batches of experiments in order to
simultaneously learn a surrogate function mapping stimulus to response and
identify the maximum of t... | computer science |
5,821 | Bayesian Optimal Active Search and Surveying | cs.LG | We consider two active binary-classification problems with atypical
objectives. In the first, active search, our goal is to actively uncover as
many members of a given class as possible. In the second, active surveying, our
goal is to actively query points to ultimately predict the proportion of a
given class. Numerous... | computer science |
5,822 | Large-Scale Feature Learning With Spike-and-Slab Sparse Coding | cs.LG | We consider the problem of object recognition with a large number of classes.
In order to overcome the low amount of labeled examples available in this
setting, we introduce a new feature learning and extraction procedure based on
a factor model we call spike-and-slab sparse coding (S3C). Prior work on S3C
has not prio... | computer science |
5,823 | On the Partition Function and Random Maximum A-Posteriori Perturbations | cs.LG | In this paper we relate the partition function to the max-statistics of
random variables. In particular, we provide a novel framework for approximating
and bounding the partition function using MAP inference on randomly perturbed
models. As a result, we can use efficient MAP solvers such as graph-cuts to
evaluate the c... | computer science |
5,824 | A Simple Algorithm for Semi-supervised Learning with Improved
Generalization Error Bound | cs.LG | In this work, we develop a simple algorithm for semi-supervised regression.
The key idea is to use the top eigenfunctions of integral operator derived from
both labeled and unlabeled examples as the basis functions and learn the
prediction function by a simple linear regression. We show that under
appropriate assumptio... | computer science |
5,825 | A Convex Relaxation for Weakly Supervised Classifiers | cs.LG | This paper introduces a general multi-class approach to weakly supervised
classification. Inferring the labels and learning the parameters of the model
is usually done jointly through a block-coordinate descent algorithm such as
expectation-maximization (EM), which may lead to local minima. To avoid this
problem, we pr... | computer science |
5,826 | The Big Data Bootstrap | cs.LG | The bootstrap provides a simple and powerful means of assessing the quality
of estimators. However, in settings involving large datasets, the computation
of bootstrap-based quantities can be prohibitively demanding. As an
alternative, we present the Bag of Little Bootstraps (BLB), a new procedure
which incorporates fea... | computer science |
5,827 | An Infinite Latent Attribute Model for Network Data | cs.LG | Latent variable models for network data extract a summary of the relational
structure underlying an observed network. The simplest possible models
subdivide nodes of the network into clusters; the probability of a link between
any two nodes then depends only on their cluster assignment. Currently
available models can b... | computer science |
5,828 | Learning Task Grouping and Overlap in Multi-task Learning | cs.LG | In the paradigm of multi-task learning, mul- tiple related prediction tasks
are learned jointly, sharing information across the tasks. We propose a
framework for multi-task learn- ing that enables one to selectively share the
information across the tasks. We assume that each task parameter vector is a
linear combi- nat... | computer science |
5,829 | Cross-Domain Multitask Learning with Latent Probit Models | cs.LG | Learning multiple tasks across heterogeneous domains is a challenging problem
since the feature space may not be the same for different tasks. We assume the
data in multiple tasks are generated from a latent common domain via sparse
domain transforms and propose a latent probit model (LPM) to jointly learn the
domain t... | computer science |
5,830 | Structured Learning from Partial Annotations | cs.LG | Structured learning is appropriate when predicting structured outputs such as
trees, graphs, or sequences. Most prior work requires the training set to
consist of complete trees, graphs or sequences. Specifying such detailed ground
truth can be tedious or infeasible for large outputs. Our main contribution is
a large m... | computer science |
5,831 | An Online Boosting Algorithm with Theoretical Justifications | cs.LG | We study the task of online boosting--combining online weak learners into an
online strong learner. While batch boosting has a sound theoretical foundation,
online boosting deserves more study from the theoretical perspective. In this
paper, we carefully compare the differences between online and batch boosting,
and pr... | computer science |
5,832 | Sparse Stochastic Inference for Latent Dirichlet allocation | cs.LG | We present a hybrid algorithm for Bayesian topic models that combines the
efficiency of sparse Gibbs sampling with the scalability of online stochastic
inference. We used our algorithm to analyze a corpus of 1.2 million books (33
billion words) with thousands of topics. Our approach reduces the bias of
variational infe... | computer science |
5,833 | Convergence of the EM Algorithm for Gaussian Mixtures with Unbalanced
Mixing Coefficients | cs.LG | The speed of convergence of the Expectation Maximization (EM) algorithm for
Gaussian mixture model fitting is known to be dependent on the amount of
overlap among the mixture components. In this paper, we study the impact of
mixing coefficients on the convergence of EM. We show that when the mixture
components exhibit ... | computer science |
5,834 | A Binary Classification Framework for Two-Stage Multiple Kernel Learning | cs.LG | With the advent of kernel methods, automating the task of specifying a
suitable kernel has become increasingly important. In this context, the
Multiple Kernel Learning (MKL) problem of finding a combination of
pre-specified base kernels that is suitable for the task at hand has received
significant attention from resea... | computer science |
5,835 | Exact Maximum Margin Structure Learning of Bayesian Networks | cs.LG | Recently, there has been much interest in finding globally optimal Bayesian
network structures. These techniques were developed for generative scores and
can not be directly extended to discriminative scores, as desired for
classification. In this paper, we propose an exact method for finding network
structures maximiz... | computer science |
5,836 | A Generative Process for Sampling Contractive Auto-Encoders | cs.LG | The contractive auto-encoder learns a representation of the input data that
captures the local manifold structure around each data point, through the
leading singular vectors of the Jacobian of the transformation from input to
representation. The corresponding singular values specify how much local
variation is plausib... | computer science |
5,837 | Rethinking Collapsed Variational Bayes Inference for LDA | cs.LG | We propose a novel interpretation of the collapsed variational Bayes
inference with a zero-order Taylor expansion approximation, called CVB0
inference, for latent Dirichlet allocation (LDA). We clarify the properties of
the CVB0 inference by using the alpha-divergence. We show that the CVB0
inference is composed of two... | computer science |
5,838 | Efficient Structured Prediction with Latent Variables for General
Graphical Models | cs.LG | In this paper we propose a unified framework for structured prediction with
latent variables which includes hidden conditional random fields and latent
structured support vector machines as special cases. We describe a local
entropy approximation for this general formulation using duality, and derive an
efficient messa... | computer science |
5,839 | Information-Theoretical Learning of Discriminative Clusters for
Unsupervised Domain Adaptation | cs.LG | We study the problem of unsupervised domain adaptation, which aims to adapt
classifiers trained on a labeled source domain to an unlabeled target domain.
Many existing approaches first learn domain-invariant features and then
construct classifiers with them. We propose a novel approach that jointly learn
the both. Spec... | computer science |
5,840 | Predicting Preference Flips in Commerce Search | cs.LG | Traditional approaches to ranking in web search follow the paradigm of
rank-by-score: a learned function gives each query-URL combination an absolute
score and URLs are ranked according to this score. This paradigm ensures that
if the score of one URL is better than another then one will always be ranked
higher than th... | computer science |
5,841 | Minimizing The Misclassification Error Rate Using a Surrogate Convex
Loss | cs.LG | We carefully study how well minimizing convex surrogate loss functions,
corresponds to minimizing the misclassification error rate for the problem of
binary classification with linear predictors. In particular, we show that
amongst all convex surrogate losses, the hinge loss gives essentially the best
possible bound, o... | computer science |
5,842 | Statistical Linear Estimation with Penalized Estimators: an Application
to Reinforcement Learning | cs.LG | Motivated by value function estimation in reinforcement learning, we study
statistical linear inverse problems, i.e., problems where the coefficients of a
linear system to be solved are observed in noise. We consider penalized
estimators, where performance is evaluated using a matrix-weighted two-norm of
the defect of ... | computer science |
5,843 | Agglomerative Bregman Clustering | cs.LG | This manuscript develops the theory of agglomerative clustering with Bregman
divergences. Geometric smoothing techniques are developed to deal with
degenerate clusters. To allow for cluster models based on exponential families
with overcomplete representations, Bregman divergences are developed for
nondifferentiable co... | computer science |
5,844 | Online Alternating Direction Method | cs.LG | Online optimization has emerged as powerful tool in large scale optimization.
In this paper, we introduce efficient online algorithms based on the
alternating directions method (ADM). We introduce a new proof technique for ADM
in the batch setting, which yields the O(1/T) convergence rate of ADM and forms
the basis of ... | computer science |
5,845 | Monte Carlo Bayesian Reinforcement Learning | cs.LG | Bayesian reinforcement learning (BRL) encodes prior knowledge of the world in
a model and represents uncertainty in model parameters by maintaining a
probability distribution over them. This paper presents Monte Carlo BRL
(MC-BRL), a simple and general approach to BRL. MC-BRL samples a priori a
finite set of hypotheses... | computer science |
5,846 | Conditional Sparse Coding and Grouped Multivariate Regression | cs.LG | We study the problem of multivariate regression where the data are naturally
grouped, and a regression matrix is to be estimated for each group. We propose
an approach in which a dictionary of low rank parameter matrices is estimated
across groups, and a sparse linear combination of the dictionary elements is
estimated... | computer science |
5,847 | The Greedy Miser: Learning under Test-time Budgets | cs.LG | As machine learning algorithms enter applications in industrial settings,
there is increased interest in controlling their cpu-time during testing. The
cpu-time consists of the running time of the algorithm and the extraction time
of the features. The latter can vary drastically when the feature set is
diverse. In this... | computer science |
5,848 | Adaptive Canonical Correlation Analysis Based On Matrix Manifolds | cs.LG | In this paper, we formulate the Canonical Correlation Analysis (CCA) problem
on matrix manifolds. This framework provides a natural way for dealing with
matrix constraints and tools for building efficient algorithms even in an
adaptive setting. Finally, an adaptive CCA algorithm is proposed and applied to
a change dete... | computer science |
5,849 | Hierarchical Exploration for Accelerating Contextual Bandits | cs.LG | Contextual bandit learning is an increasingly popular approach to optimizing
recommender systems via user feedback, but can be slow to converge in practice
due to the need for exploring a large feature space. In this paper, we propose
a coarse-to-fine hierarchical approach for encoding prior knowledge that
drastically ... | computer science |
5,850 | Regularizers versus Losses for Nonlinear Dimensionality Reduction: A
Factored View with New Convex Relaxations | cs.LG | We demonstrate that almost all non-parametric dimensionality reduction
methods can be expressed by a simple procedure: regularized loss minimization
plus singular value truncation. By distinguishing the role of the loss and
regularizer in such a process, we recover a factored perspective that reveals
some gaps in the c... | computer science |
5,851 | Exponential Regret Bounds for Gaussian Process Bandits with
Deterministic Observations | cs.LG | This paper analyzes 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 proved that t... | computer science |
5,852 | Batch Active Learning via Coordinated Matching | cs.LG | Most prior work on active learning of classifiers has focused on sequentially
selecting one unlabeled example at a time to be labeled in order to reduce the
overall labeling effort. In many scenarios, however, it is desirable to label
an entire batch of examples at once, for example, when labels can be acquired
in para... | computer science |
5,853 | On the Sample Complexity of Reinforcement Learning with a Generative
Model | cs.LG | We consider the problem of learning the optimal action-value function in the
discounted-reward Markov decision processes (MDPs). We prove a new PAC bound on
the sample-complexity of model-based value iteration algorithm in the presence
of the generative model, which indicates that for an MDP with N state-action
pairs a... | computer science |
5,854 | An Iterative Locally Linear Embedding Algorithm | cs.LG | Local Linear embedding (LLE) is a popular dimension reduction method. In this
paper, we first show LLE with nonnegative constraint is equivalent to the
widely used Laplacian embedding. We further propose to iterate the two steps in
LLE repeatedly to improve the results. Thirdly, we relax the kNN constraint of
LLE and p... | computer science |
5,855 | Estimating the Hessian by Back-propagating Curvature | cs.LG | In this work we develop Curvature Propagation (CP), a general technique for
efficiently computing unbiased approximations of the Hessian of any function
that is computed using a computational graph. At the cost of roughly two
gradient evaluations, CP can give a rank-1 approximation of the whole Hessian,
and can be repe... | computer science |
5,856 | Bayesian Efficient Multiple Kernel Learning | cs.LG | Multiple kernel learning algorithms are proposed to combine kernels in order
to obtain a better similarity measure or to integrate feature representations
coming from different data sources. Most of the previous research on such
methods is focused on the computational efficiency issue. However, it is still
not feasible... | computer science |
5,857 | Semi-Supervised Collective Classification via Hybrid Label
Regularization | cs.LG | Many classification problems involve data instances that are interlinked with
each other, such as webpages connected by hyperlinks. Techniques for
"collective classification" (CC) often increase accuracy for such data graphs,
but usually require a fully-labeled training graph. In contrast, we examine how
to improve the... | computer science |
5,858 | Inferring Latent Structure From Mixed Real and Categorical Relational
Data | cs.LG | We consider analysis of relational data (a matrix), in which the rows
correspond to subjects (e.g., people) and the columns correspond to attributes.
The elements of the matrix may be a mix of real and categorical. Each subject
and attribute is characterized by a latent binary feature vector, and an
inferred matrix map... | computer science |
5,859 | On Causal and Anticausal Learning | cs.LG | We consider the problem of function estimation in the case where an
underlying causal model can be inferred. This has implications for popular
scenarios such as covariate shift, concept drift, transfer learning and
semi-supervised learning. We argue that causal knowledge may facilitate some
approaches for a given probl... | computer science |
5,860 | An Efficient Approach to Sparse Linear Discriminant Analysis | cs.LG | We present a novel approach to the formulation and the resolution of sparse
Linear Discriminant Analysis (LDA). Our proposal, is based on penalized Optimal
Scoring. It has an exact equivalence with penalized LDA, contrary to the
multi-class approaches based on the regression of class indicator that have
been proposed s... | computer science |
5,861 | A Split-Merge Framework for Comparing Clusterings | cs.LG | Clustering evaluation measures are frequently used to evaluate the
performance of algorithms. However, most measures are not properly normalized
and ignore some information in the inherent structure of clusterings. We model
the relation between two clusterings as a bipartite graph and propose a general
component-based ... | computer science |
5,862 | Similarity Learning for Provably Accurate Sparse Linear Classification | cs.LG | In recent years, the crucial importance of metrics in machine learning
algorithms has led to an increasing interest for optimizing distance and
similarity functions. Most of the state of the art focus on learning
Mahalanobis distances (requiring to fulfill a constraint of positive
semi-definiteness) for use in a local ... | computer science |
5,863 | Discovering Support and Affiliated Features from Very High Dimensions | cs.LG | In this paper, a novel learning paradigm is presented to automatically
identify groups of informative and correlated features from very high
dimensions. Specifically, we explicitly incorporate correlation measures as
constraints and then propose an efficient embedded feature selection method
using recently developed cu... | computer science |
5,864 | Maximum Margin Output Coding | cs.LG | In this paper we study output coding for multi-label prediction. For a
multi-label output coding to be discriminative, it is important that codewords
for different label vectors are significantly different from each other. In the
meantime, unlike in traditional coding theory, codewords in output coding are
to be predic... | computer science |
5,865 | The Landmark Selection Method for Multiple Output Prediction | cs.LG | Conditional modeling x \to y is a central problem in machine learning. A
substantial research effort is devoted to such modeling when x is high
dimensional. We consider, instead, the case of a high dimensional y, where x is
either low dimensional or high dimensional. Our approach is based on selecting
a small subset y_... | computer science |
5,866 | A Dantzig Selector Approach to Temporal Difference Learning | cs.LG | LSTD is a popular algorithm for value function approximation. Whenever the
number of features is larger than the number of samples, it must be paired with
some form of regularization. In particular, L1-regularization methods tend to
perform feature selection by promoting sparsity, and thus, are well-suited for
high-dim... | computer science |
5,867 | Subgraph Matching Kernels for Attributed Graphs | cs.LG | We propose graph kernels based on subgraph matchings, i.e.
structure-preserving bijections between subgraphs. While recently proposed
kernels based on common subgraphs (Wale et al., 2008; Shervashidze et al.,
2009) in general can not be applied to attributed graphs, our approach allows
to rate mappings of subgraphs by ... | computer science |
5,868 | Greedy Algorithms for Sparse Reinforcement Learning | cs.LG | Feature selection and regularization are becoming increasingly prominent
tools in the efforts of the reinforcement learning (RL) community to expand the
reach and applicability of RL. One approach to the problem of feature selection
is to impose a sparsity-inducing form of regularization on the learning method.
Recent ... | computer science |
5,869 | Flexible Modeling of Latent Task Structures in Multitask Learning | cs.LG | Multitask learning algorithms are typically designed assuming some fixed, a
priori known latent structure shared by all the tasks. However, it is usually
unclear what type of latent task structure is the most appropriate for a given
multitask learning problem. Ideally, the "right" latent task structure should
be learne... | computer science |
5,870 | A concentration theorem for projections | cs.LG | X in R^D has mean zero and finite second moments. We show that there is a
precise sense in which almost all linear projections of X into R^d (for d < D)
look like a scale-mixture of spherical Gaussians -- specifically, a mixture of
distributions N(0, sigma^2 I_d) where the weight of the particular sigma
component is P ... | computer science |
5,871 | Discriminative Learning via Semidefinite Probabilistic Models | cs.LG | Discriminative linear models are a popular tool in machine learning. These
can be generally divided into two types: The first is linear classifiers, such
as support vector machines, which are well studied and provide state-of-the-art
results. One shortcoming of these models is that their output (known as the
'margin') ... | computer science |
5,872 | Convex Structure Learning for Bayesian Networks: Polynomial Feature
Selection and Approximate Ordering | cs.LG | We present a new approach to learning the structure and parameters of a
Bayesian network based on regularized estimation in an exponential family
representation. Here we show that, given a fixed variable order, the optimal
structure and parameters can be learned efficiently, even without restricting
the size of the par... | computer science |
5,873 | Predicting Conditional Quantiles via Reduction to Classification | cs.LG | We show how to reduce the process of predicting general order statistics (and
the median in particular) to solving classification. The accompanying
theoretical statement shows that the regret of the classifier bounds the regret
of the quantile regression under a quantile loss. We also test this reduction
empirically ag... | computer science |
5,874 | Bayesian Multicategory Support Vector Machines | cs.LG | We show that the multi-class support vector machine (MSVM) proposed by Lee
et. al. (2004), can be viewed as a MAP estimation procedure under an
appropriate probabilistic interpretation of the classifier. We also show that
this interpretation can be extended to a hierarchical Bayesian architecture and
to a fully-Bayesia... | computer science |
5,875 | Bayesian Random Fields: The Bethe-Laplace Approximation | cs.LG | While learning the maximum likelihood value of parameters of an undirected
graphical model is hard, modelling the posterior distribution over parameters
given data is harder. Yet, undirected models are ubiquitous in computer vision
and text modelling (e.g. conditional random fields). But where Bayesian
approaches for d... | computer science |
5,876 | Ranking by Dependence - A Fair Criteria | cs.LG | Estimating the dependences between random variables, and ranking them
accordingly, is a prevalent problem in machine learning. Pursuing frequentist
and information-theoretic approaches, we first show that the p-value and the
mutual information can fail even in simplistic situations. We then propose two
conditions for r... | computer science |
5,877 | Variable noise and dimensionality reduction for sparse Gaussian
processes | cs.LG | The sparse pseudo-input Gaussian process (SPGP) is a new approximation method
for speeding up GP regression in the case of a large number of data points N.
The approximation is controlled by the gradient optimization of a small set of
M `pseudo-inputs', thereby reducing complexity from N^3 to NM^2. One limitation
of th... | computer science |
5,878 | Cumulative Step-size Adaptation on Linear Functions | cs.LG | The CSA-ES is an Evolution Strategy with Cumulative Step size Adaptation,
where the step size is adapted measuring the length of a so-called cumulative
path. The cumulative path is a combination of the previous steps realized by
the algorithm, where the importance of each step decreases with time. This
article studies ... | computer science |
5,879 | Cost-Sensitive Support Vector Machines | cs.LG | A new procedure for learning cost-sensitive SVM(CS-SVM) classifiers is
proposed. The SVM hinge loss is extended to the cost sensitive setting, and the
CS-SVM is derived as the minimizer of the associated risk. The extension of the
hinge loss draws on recent connections between risk minimization and
probability elicitat... | computer science |
5,880 | On Some Integrated Approaches to Inference | stat.ML | We present arguments for the formulation of unified approach to different
standard continuous inference methods from partial information. It is claimed
that an explicit partition of information into a priori (prior knowledge) and a
posteriori information (data) is an important way of standardizing inference
approaches ... | computer science |
5,881 | Excess risk bounds for multitask learning with trace norm regularization | stat.ML | Trace norm regularization is a popular method of multitask learning. We give
excess risk bounds with explicit dependence on the number of tasks, the number
of examples per task and properties of the data distribution. The bounds are
independent of the dimension of the input space, which may be infinite as in
the case o... | computer science |
5,882 | A class of random fields on complete graphs with tractable partition
function | cs.LG | The aim of this short note is to draw attention to a method by which the
partition function and marginal probabilities for a certain class of random
fields on complete graphs can be computed in polynomial time. This class
includes Ising models with homogeneous pairwise potentials but arbitrary
(inhomogeneous) unary pot... | computer science |
5,883 | PAC-Bayesian Learning and Domain Adaptation | stat.ML | In machine learning, Domain Adaptation (DA) arises when the distribution gen-
erating the test (target) data differs from the one generating the learning
(source) data. It is well known that DA is an hard task even under strong
assumptions, among which the covariate-shift where the source and target
distributions diver... | computer science |
5,884 | Bayesian Hierarchical Mixtures of Experts | cs.LG | The Hierarchical Mixture of Experts (HME) is a well-known tree-based model
for regression and classification, based on soft probabilistic splits. In its
original formulation it was trained by maximum likelihood, and is therefore
prone to over-fitting. Furthermore the maximum likelihood framework offers no
natural metri... | computer science |
5,885 | The Information Bottleneck EM Algorithm | cs.LG | Learning with hidden variables is a central challenge in probabilistic
graphical models that has important implications for many real-life problems.
The classical approach is using the Expectation Maximization (EM) algorithm.
This algorithm, however, can get trapped in local maxima. In this paper we
explore a new appro... | computer science |
5,886 | On Information Regularization | cs.LG | We formulate a principle for classification with the knowledge of the
marginal distribution over the data points (unlabeled data). The principle is
cast in terms of Tikhonov style regularization where the regularization penalty
articulates the way in which the marginal density should constrain otherwise
unrestricted co... | computer science |
5,887 | Budgeted Learning of Naive-Bayes Classifiers | cs.LG | Frequently, acquiring training data has an associated cost. We consider the
situation where the learner may purchase data during training, subject TO a
budget. IN particular, we examine the CASE WHERE each feature label has an
associated cost, AND the total cost OF ALL feature labels acquired during
training must NOT e... | computer science |
5,888 | Learning Riemannian Metrics | cs.LG | We propose a solution to the problem of estimating a Riemannian metric
associated with a given differentiable manifold. The metric learning problem is
based on minimizing the relative volume of a given set of points. We derive the
details for a family of metrics on the multinomial simplex. The resulting
metric has appl... | computer science |
5,889 | Sufficient Dimensionality Reduction with Irrelevant Statistics | cs.LG | The problem of finding a reduced dimensionality representation of categorical
variables while preserving their most relevant characteristics is fundamental
for the analysis of complex data. Specifically, given a co-occurrence matrix of
two variables, one often seeks a compact representation of one variable which
preser... | computer science |
5,890 | Locally Weighted Naive Bayes | cs.LG | Despite its simplicity, the naive Bayes classifier has surprised machine
learning researchers by exhibiting good performance on a variety of learning
problems. Encouraged by these results, researchers have looked to overcome
naive Bayes primary weakness - attribute independence - and improve the
performance of the algo... | computer science |
5,891 | A Distance-Based Branch and Bound Feature Selection Algorithm | cs.LG | There is no known efficient method for selecting k Gaussian features from n
which achieve the lowest Bayesian classification error. We show an example of
how greedy algorithms faced with this task are led to give results that are not
optimal. This motivates us to propose a more robust approach. We present a
Branch and ... | computer science |
5,892 | On the Convergence of Bound Optimization Algorithms | cs.LG | Many practitioners who use the EM algorithm complain that it is sometimes
slow. When does this happen, and what can be done about it? In this paper, we
study the general class of bound optimization algorithms - including
Expectation-Maximization, Iterative Scaling and CCCP - and their relationship
to direct optimizatio... | computer science |
5,893 | Automated Analytic Asymptotic Evaluation of the Marginal Likelihood for
Latent Models | cs.LG | We present and implement two algorithms for analytic asymptotic evaluation of
the marginal likelihood of data given a Bayesian network with hidden nodes. As
shown by previous work, this evaluation is particularly hard for latent
Bayesian network models, namely networks that include hidden variables, where
asymptotic ap... | computer science |
5,894 | Learning Generative Models of Similarity Matrices | cs.LG | We describe a probabilistic (generative) view of affinity matrices along with
inference algorithms for a subclass of problems associated with data
clustering. This probabilistic view is helpful in understanding different
models and algorithms that are based on affinity functions OF the data. IN
particular, we show how(... | computer science |
5,895 | Learning Continuous Time Bayesian Networks | cs.LG | Continuous time Bayesian networks (CTBNs) describe structured stochastic
processes with finitely many states that evolve over continuous time. A CTBN is
a directed (possibly cyclic) dependency graph over a set of variables, each of
which represents a finite state continuous time Markov process whose transition
model is... | computer science |
5,896 | Efficiently Inducing Features of Conditional Random Fields | cs.LG | Conditional Random Fields (CRFs) are undirected graphical models, a special
case of which correspond to conditionally-trained finite state machines. A key
advantage of these models is their great flexibility to include a wide array of
overlapping, multi-granularity, non-independent features of the input. In face
of thi... | computer science |
5,897 | Markov Random Walk Representations with Continuous Distributions | cs.LG | Representations based on random walks can exploit discrete data distributions
for clustering and classification. We extend such representations from discrete
to continuous distributions. Transition probabilities are now calculated using
a diffusion equation with a diffusion coefficient that inversely depends on the
dat... | computer science |
5,898 | Stochastic complexity of Bayesian networks | cs.LG | Bayesian networks are now being used in enormous fields, for example,
diagnosis of a system, data mining, clustering and so on. In spite of their
wide range of applications, the statistical properties have not yet been
clarified, because the models are nonidentifiable and non-regular. In a
Bayesian network, the set of ... | computer science |
5,899 | A Generalized Mean Field Algorithm for Variational Inference in
Exponential Families | cs.LG | The mean field methods, which entail approximating intractable probability
distributions variationally with distributions from a tractable family, enjoy
high efficiency, guaranteed convergence, and provide lower bounds on the true
likelihood. But due to requirement for model-specific derivation of the
optimization equa... | computer science |
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