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5,500 | The Natural Gradient by Analogy to Signal Whitening, and Recipes and
Tricks for its Use | cs.LG | The natural gradient allows for more efficient gradient descent by removing
dependencies and biases inherent in a function's parameterization. Several
papers present the topic thoroughly and precisely. It remains a very difficult
idea to get your head around however. The intent of this note is to provide
simple intuiti... | computer science |
5,501 | A Generalized Kernel Approach to Structured Output Learning | stat.ML | We study the problem of structured output learning from a regression
perspective. We first provide a general formulation of the kernel dependency
estimation (KDE) problem using operator-valued kernels. We show that some of
the existing formulations of this problem are special cases of our framework.
We then propose a c... | computer science |
5,502 | On the Identifiability of the Post-Nonlinear Causal Model | stat.ML | By taking into account the nonlinear effect of the cause, the inner noise
effect, and the measurement distortion effect in the observed variables, the
post-nonlinear (PNL) causal model has demonstrated its excellent performance in
distinguishing the cause from effect. However, its identifiability has not been
properly ... | computer science |
5,503 | The Infinite Latent Events Model | stat.ML | We present the Infinite Latent Events Model, a nonparametric hierarchical
Bayesian distribution over infinite dimensional Dynamic Bayesian Networks with
binary state representations and noisy-OR-like transitions. The distribution
can be used to learn structure in discrete timeseries data by simultaneously
inferring a s... | computer science |
5,504 | Herding Dynamic Weights for Partially Observed Random Field Models | cs.LG | Learning the parameters of a (potentially partially observable) random field
model is intractable in general. Instead of focussing on a single optimal
parameter value we propose to treat parameters as dynamical quantities. We
introduce an algorithm to generate complex dynamics for parameters and (both
visible and hidde... | computer science |
5,505 | Temporal-Difference Networks for Dynamical Systems with Continuous
Observations and Actions | cs.LG | Temporal-difference (TD) networks are a class of predictive state
representations that use well-established TD methods to learn models of
partially observable dynamical systems. Previous research with TD networks has
dealt only with dynamical systems with finite sets of observations and actions.
We present an algorithm... | computer science |
5,506 | Which Spatial Partition Trees are Adaptive to Intrinsic Dimension? | stat.ML | Recent theory work has found that a special type of spatial partition tree -
called a random projection tree - is adaptive to the intrinsic dimension of the
data from which it is built. Here we examine this same question, with a
combination of theory and experiments, for a broader class of trees that
includes k-d trees... | computer science |
5,507 | Computing Posterior Probabilities of Structural Features in Bayesian
Networks | cs.LG | We study the problem of learning Bayesian network structures from data.
Koivisto and Sood (2004) and Koivisto (2006) presented algorithms that can
compute the exact marginal posterior probability of a subnetwork, e.g., a
single edge, in O(n2n) time and the posterior probabilities for all n(n-1)
potential edges in O(n2n... | computer science |
5,508 | Products of Hidden Markov Models: It Takes N>1 to Tango | cs.LG | Products of Hidden Markov Models(PoHMMs) are an interesting class of
generative models which have received little attention since their
introduction. This maybe in part due to their more computationally expensive
gradient-based learning algorithm,and the intractability of computing the log
likelihood of sequences under... | computer science |
5,509 | Virtual Vector Machine for Bayesian Online Classification | cs.LG | In a typical online learning scenario, a learner is required to process a
large data stream using a small memory buffer. Such a requirement is usually in
conflict with a learner's primary pursuit of prediction accuracy. To address
this dilemma, we introduce a novel Bayesian online classi cation algorithm,
called the Vi... | computer science |
5,510 | Group Sparse Priors for Covariance Estimation | stat.ML | Recently it has become popular to learn sparse Gaussian graphical models
(GGMs) by imposing l1 or group l1,2 penalties on the elements of the precision
matrix. Thispenalized likelihood approach results in a tractable convex
optimization problem. In this paper, we reinterpret these results as performing
MAP estimation u... | computer science |
5,511 | Domain Knowledge Uncertainty and Probabilistic Parameter Constraints | cs.LG | Incorporating domain knowledge into the modeling process is an effective way
to improve learning accuracy. However, as it is provided by humans, domain
knowledge can only be specified with some degree of uncertainty. We propose to
explicitly model such uncertainty through probabilistic constraints over the
parameter sp... | computer science |
5,512 | Multiple Source Adaptation and the Renyi Divergence | cs.LG | This paper presents a novel theoretical study of the general problem of
multiple source adaptation using the notion of Renyi divergence. Our results
build on our previous work [12], but significantly broaden the scope of that
work in several directions. We extend previous multiple source loss guarantees
based on distri... | computer science |
5,513 | Interpretation and Generalization of Score Matching | cs.LG | Score matching is a recently developed parameter learning method that is
particularly effective to complicated high dimensional density models with
intractable partition functions. In this paper, we study two issues that have
not been completely resolved for score matching. First, we provide a formal
link between maxim... | computer science |
5,514 | Identifying confounders using additive noise models | stat.ML | We propose a method for inferring the existence of a latent common cause
('confounder') of two observed random variables. The method assumes that the
two effects of the confounder are (possibly nonlinear) functions of the
confounder plus independent, additive noise. We discuss under which conditions
the model is identi... | computer science |
5,515 | Correlated Non-Parametric Latent Feature Models | cs.LG | We are often interested in explaining data through a set of hidden factors or
features. When the number of hidden features is unknown, the Indian Buffet
Process (IBP) is a nonparametric latent feature model that does not bound the
number of active features in dataset. However, the IBP assumes that all latent
features a... | computer science |
5,516 | L2 Regularization for Learning Kernels | cs.LG | The choice of the kernel is critical to the success of many learning
algorithms but it is typically left to the user. Instead, the training data can
be used to learn the kernel by selecting it out of a given family, such as that
of non-negative linear combinations of p base kernels, constrained by a trace
or L1 regular... | computer science |
5,517 | Optimization of Structured Mean Field Objectives | stat.ML | In intractable, undirected graphical models, an intuitive way of creating
structured mean field approximations is to select an acyclic tractable
subgraph. We show that the hardness of computing the objective function and
gradient of the mean field objective qualitatively depends on a simple graph
property. If the tract... | computer science |
5,518 | Alternating Projections for Learning with Expectation Constraints | cs.LG | We present an objective function for learning with unlabeled data that
utilizes auxiliary expectation constraints. We optimize this objective function
using a procedure that alternates between information and moment projections.
Our method provides an alternate interpretation of the posterior regularization
framework (... | computer science |
5,519 | On Smoothing and Inference for Topic Models | cs.LG | Latent Dirichlet analysis, or topic modeling, is a flexible latent variable
framework for modeling high-dimensional sparse count data. Various learning
algorithms have been developed in recent years, including collapsed Gibbs
sampling, variational inference, and maximum a posteriori estimation, and this
variety motivat... | computer science |
5,520 | Multiple Identifications in Multi-Armed Bandits | cs.LG | We study the problem of identifying the top $m$ arms in a multi-armed bandit
game. Our proposed solution relies on a new algorithm based on successive
rejects of the seemingly bad arms, and successive accepts of the good ones.
This algorithmic contribution allows to tackle other multiple identifications
settings that w... | computer science |
5,521 | Thompson Sampling: An Asymptotically Optimal Finite Time Analysis | stat.ML | The question of the optimality of Thompson Sampling for solving the
stochastic multi-armed bandit problem had been open since 1933. In this paper
we answer it positively for the case of Bernoulli rewards by providing the
first finite-time analysis that matches the asymptotic rate given in the Lai
and Robbins lower boun... | computer science |
5,522 | New Analysis and Algorithm for Learning with Drifting Distributions | cs.LG | We present a new analysis of the problem of learning with drifting
distributions in the batch setting using the notion of discrepancy. We prove
learning bounds based on the Rademacher complexity of the hypothesis set and
the discrepancy of distributions both for a drifting PAC scenario and a
tracking scenario. Our boun... | computer science |
5,523 | Conditional mean embeddings as regressors - supplementary | cs.LG | We demonstrate an equivalence between reproducing kernel Hilbert space (RKHS)
embeddings of conditional distributions and vector-valued regressors. This
connection introduces a natural regularized loss function which the RKHS
embeddings minimise, providing an intuitive understanding of the embeddings and
a justificatio... | computer science |
5,524 | Efficient Sparse Group Feature Selection via Nonconvex Optimization | cs.LG | Sparse feature selection has been demonstrated to be effective in handling
high-dimensional data. While promising, most of the existing works use convex
methods, which may be suboptimal in terms of the accuracy of feature selection
and parameter estimation. In this paper, we expand a nonconvex paradigm to
sparse group ... | computer science |
5,525 | Measurability Aspects of the Compactness Theorem for Sample Compression
Schemes | stat.ML | It was proved in 1998 by Ben-David and Litman that a concept space has a
sample compression scheme of size d if and only if every finite subspace has a
sample compression scheme of size d. In the compactness theorem, measurability
of the hypotheses of the created sample compression scheme is not guaranteed;
at the same... | computer science |
5,526 | Learning Dictionaries with Bounded Self-Coherence | stat.ML | Sparse coding in learned dictionaries has been established as a successful
approach for signal denoising, source separation and solving inverse problems
in general. A dictionary learning method adapts an initial dictionary to a
particular signal class by iteratively computing an approximate factorization
of a training ... | computer science |
5,527 | Implicit Density Estimation by Local Moment Matching to Sample from
Auto-Encoders | cs.LG | Recent work suggests that some auto-encoder variants do a good job of
capturing the local manifold structure of the unknown data generating density.
This paper contributes to the mathematical understanding of this phenomenon and
helps define better justified sampling algorithms for deep learning based on
auto-encoder v... | computer science |
5,528 | Density-Difference Estimation | cs.LG | We address the problem of estimating the difference between two probability
densities. A naive approach is a two-step procedure of first estimating two
densities separately and then computing their difference. However, such a
two-step procedure does not necessarily work well because the first step is
performed without ... | computer science |
5,529 | Surrogate Regret Bounds for Bipartite Ranking via Strongly Proper Losses | cs.LG | The problem of bipartite ranking, where instances are labeled positive or
negative and the goal is to learn a scoring function that minimizes the
probability of mis-ranking a pair of positive and negative instances (or
equivalently, that maximizes the area under the ROC curve), has been widely
studied in recent years. ... | computer science |
5,530 | Robust Dequantized Compressive Sensing | stat.ML | We consider the reconstruction problem in compressed sensing in which the
observations are recorded in a finite number of bits. They may thus contain
quantization errors (from being rounded to the nearest representable value) and
saturation errors (from being outside the range of representable values). Our
formulation ... | computer science |
5,531 | Unsupervised spectral learning | cs.LG | In spectral clustering and spectral image segmentation, the data is partioned
starting from a given matrix of pairwise similarities S. the matrix S is
constructed by hand, or learned on a separate training set. In this paper we
show how to achieve spectral clustering in unsupervised mode. Our algorithm
starts with a se... | computer science |
5,532 | Learning from Sparse Data by Exploiting Monotonicity Constraints | cs.LG | When training data is sparse, more domain knowledge must be incorporated into
the learning algorithm in order to reduce the effective size of the hypothesis
space. This paper builds on previous work in which knowledge about qualitative
monotonicities was formally represented and incorporated into learning
algorithms (e... | computer science |
5,533 | Learning Factor Graphs in Polynomial Time & Sample Complexity | cs.LG | We study computational and sample complexity of parameter and structure
learning in graphical models. Our main result shows that the class of factor
graphs with bounded factor size and bounded connectivity can be learned in
polynomial time and polynomial number of samples, assuming that the data is
generated by a netwo... | computer science |
5,534 | On the Detection of Concept Changes in Time-Varying Data Stream by
Testing Exchangeability | cs.LG | A martingale framework for concept change detection based on testing data
exchangeability was recently proposed (Ho, 2005). In this paper, we describe
the proposed change-detection test based on the Doob's Maximal Inequality and
show that it is an approximation of the sequential probability ratio test
(SPRT). The relat... | computer science |
5,535 | Maximum Margin Bayesian Networks | cs.LG | We consider the problem of learning Bayesian network classifiers that
maximize the marginover a set of classification variables. We find that this
problem is harder for Bayesian networks than for undirected graphical models
like maximum margin Markov networks. The main difficulty is that the parameters
in a Bayesian ne... | computer science |
5,536 | Learning about individuals from group statistics | cs.LG | We propose a new problem formulation which is similar to, but more
informative than, the binary multiple-instance learning problem. In this
setting, we are given groups of instances (described by feature vectors) along
with estimates of the fraction of positively-labeled instances per group. The
task is to learn an ins... | computer science |
5,537 | Obtaining Calibrated Probabilities from Boosting | cs.LG | Boosted decision trees typically yield good accuracy, precision, and ROC
area. However, because the outputs from boosting are not well calibrated
posterior probabilities, boosting yields poor squared error and cross-entropy.
We empirically demonstrate why AdaBoost predicts distorted probabilities and
examine three cali... | computer science |
5,538 | Piecewise Training for Undirected Models | cs.LG | For many large undirected models that arise in real-world applications, exact
maximumlikelihood training is intractable, because it requires computing
marginal distributions of the model. Conditional training is even more
difficult, because the partition function depends not only on the parameters,
but also on the obse... | computer science |
5,539 | The DLR Hierarchy of Approximate Inference | cs.LG | We propose a hierarchy for approximate inference based on the Dobrushin,
Lanford, Ruelle (DLR) equations. This hierarchy includes existing algorithms,
such as belief propagation, and also motivates novel algorithms such as
factorized neighbors (FN) algorithms and variants of mean field (MF)
algorithms. In particular, w... | computer science |
5,540 | A Function Approximation Approach to Estimation of Policy Gradient for
POMDP with Structured Policies | cs.LG | We consider the estimation of the policy gradient in partially observable
Markov decision processes (POMDP) with a special class of structured policies
that are finite-state controllers. We show that the gradient estimation can be
done in the Actor-Critic framework, by making the critic compute a "value"
function that ... | computer science |
5,541 | Optimal rates for first-order stochastic convex optimization under
Tsybakov noise condition | cs.LG | We focus on the problem of minimizing a convex function $f$ over a convex set
$S$ given $T$ queries to a stochastic first order oracle. We argue that the
complexity of convex minimization is only determined by the rate of growth of
the function around its minimizer $x^*_{f,S}$, as quantified by a Tsybakov-like
noise co... | computer science |
5,542 | Improved brain pattern recovery through ranking approaches | cs.LG | Inferring the functional specificity of brain regions from functional
Magnetic Resonance Images (fMRI) data is a challenging statistical problem.
While the General Linear Model (GLM) remains the standard approach for brain
mapping, supervised learning techniques (a.k.a.} decoding) have proven to be
useful to capture mu... | computer science |
5,543 | Ensemble Clustering with Logic Rules | stat.ML | In this article, the logic rule ensembles approach to supervised learning is
applied to the unsupervised or semi-supervised clustering. Logic rules which
were obtained by combining simple conjunctive rules are used to partition the
input space and an ensemble of these rules is used to define a similarity
matrix. Simila... | computer science |
5,544 | The Minimum Information Principle for Discriminative Learning | cs.LG | Exponential models of distributions are widely used in machine learning for
classiffication and modelling. It is well known that they can be interpreted as
maximum entropy models under empirical expectation constraints. In this work,
we argue that for classiffication tasks, mutual information is a more suitable
informa... | computer science |
5,545 | Algebraic Statistics in Model Selection | cs.LG | We develop the necessary theory in computational algebraic geometry to place
Bayesian networks into the realm of algebraic statistics. We present an
algebra{statistics dictionary focused on statistical modeling. In particular,
we link the notion of effiective dimension of a Bayesian network with the
notion of algebraic... | computer science |
5,546 | On-line Prediction with Kernels and the Complexity Approximation
Principle | cs.LG | The paper describes an application of Aggregating Algorithm to the problem of
regression. It generalizes earlier results concerned with plain linear
regression to kernel techniques and presents an on-line algorithm which
performs nearly as well as any oblivious kernel predictor. The paper contains
the derivation of an ... | computer science |
5,547 | Applying Discrete PCA in Data Analysis | cs.LG | Methods for analysis of principal components in discrete data have existed
for some time under various names such as grade of membership modelling,
probabilistic latent semantic analysis, and genotype inference with admixture.
In this paper we explore a number of extensions to the common theory, and
present some applic... | computer science |
5,548 | Exponential Families for Conditional Random Fields | cs.LG | In this paper we de ne conditional random elds in reproducing kernel Hilbert
spaces and show connections to Gaussian Process classi cation. More speci
cally, we prove decomposition results for undirected graphical models and we
give constructions for kernels. Finally we present e cient means of solving the
optimization... | computer science |
5,549 | "Ideal Parent" Structure Learning for Continuous Variable Networks | cs.LG | In recent years, there is a growing interest in learning Bayesian networks
with continuous variables. Learning the structure of such networks is a
computationally expensive procedure, which limits most applications to
parameter learning. This problem is even more acute when learning networks with
hidden variables. We p... | computer science |
5,550 | Bayesian Learning in Undirected Graphical Models: Approximate MCMC
algorithms | cs.LG | Bayesian learning in undirected graphical models|computing posterior
distributions over parameters and predictive quantities is exceptionally
difficult. We conjecture that for general undirected models, there are no
tractable MCMC (Markov Chain Monte Carlo) schemes giving the correct
equilibrium distribution over param... | computer science |
5,551 | Active Model Selection | cs.LG | Classical learning assumes the learner is given a labeled data sample, from
which it learns a model. The field of Active Learning deals with the situation
where the learner begins not with a training sample, but instead with resources
that it can use to obtain information to help identify the optimal model. To
better u... | computer science |
5,552 | An Extended Cencov-Campbell Characterization of Conditional Information
Geometry | cs.LG | We formulate and prove an axiomatic characterization of conditional
information geometry, for both the normalized and the nonnormalized cases. This
characterization extends the axiomatic derivation of the Fisher geometry by
Cencov and Campbell to the cone of positive conditional models, and as a
special case to the man... | computer science |
5,553 | Conditional Chow-Liu Tree Structures for Modeling Discrete-Valued Vector
Time Series | cs.LG | We consider the problem of modeling discrete-valued vector time series data
using extensions of Chow-Liu tree models to capture both dependencies across
time and dependencies across variables. Conditional Chow-Liu tree models are
introduced, as an extension to standard Chow-Liu trees, for modeling
conditional rather th... | computer science |
5,554 | A Generative Bayesian Model for Aggregating Experts' Probabilities | cs.LG | In order to improve forecasts, a decisionmaker often combines probabilities
given by various sources, such as human experts and machine learning
classifiers. When few training data are available, aggregation can be improved
by incorporating prior knowledge about the event being forecasted and about
salient properties o... | computer science |
5,555 | Dynamical Systems Trees | cs.LG | We propose dynamical systems trees (DSTs) as a flexible class of models for
describing multiple processes that interact via a hierarchy of aggregating
parent chains. DSTs extend Kalman filters, hidden Markov models and nonlinear
dynamical systems to an interactive group scenario. Various individual
processes interact a... | computer science |
5,556 | Similarity-Driven Cluster Merging Method for Unsupervised Fuzzy
Clustering | cs.LG | In this paper, a similarity-driven cluster merging method is proposed for
unsuper-vised fuzzy clustering. The cluster merging method is used to resolve
the problem of cluster validation. Starting with an overspecified number of
clusters in the data, pairs of similar clusters are merged based on the
proposed similarity-... | computer science |
5,557 | Graph partition strategies for generalized mean field inference | cs.LG | An autonomous variational inference algorithm for arbitrary graphical models
requires the ability to optimize variational approximations over the space of
model parameters as well as over the choice of tractable families used for the
variational approximation. In this paper, we present a novel combination of
graph part... | computer science |
5,558 | Factored Latent Analysis for far-field tracking data | cs.LG | This paper uses Factored Latent Analysis (FLA) to learn a factorized,
segmental representation for observations of tracked objects over time.
Factored Latent Analysis is latent class analysis in which the observation
space is subdivided and each aspect of the original space is represented by a
separate latent class mod... | computer science |
5,559 | Variational Chernoff Bounds for Graphical Models | cs.LG | Recent research has made significant progress on the problem of bounding log
partition functions for exponential family graphical models. Such bounds have
associated dual parameters that are often used as heuristic estimates of the
marginal probabilities required in inference and learning. However these
variational est... | computer science |
5,560 | On the Statistical Efficiency of $\ell_{1,p}$ Multi-Task Learning of
Gaussian Graphical Models | cs.LG | In this paper, we present $\ell_{1,p}$ multi-task structure learning for
Gaussian graphical models. We analyze the sufficient number of samples for the
correct recovery of the support union and edge signs. We also analyze the
necessary number of samples for any conceivable method by providing
information-theoretic lowe... | computer science |
5,561 | Local stability of Belief Propagation algorithm with multiple fixed
points | stat.ML | A number of problems in statistical physics and computer science can be
expressed as the computation of marginal probabilities over a Markov random
field. Belief propagation, an iterative message-passing algorithm, computes
exactly such marginals when the underlying graph is a tree. But it has gained
its popularity as ... | computer science |
5,562 | Proceedings of the 29th International Conference on Machine Learning
(ICML-12) | cs.LG | This is an index to the papers that appear in the Proceedings of the 29th
International Conference on Machine Learning (ICML-12). The conference was held
in Edinburgh, Scotland, June 27th - July 3rd, 2012. | computer science |
5,563 | Fast nonparametric classification based on data depth | stat.ML | A new procedure, called DDa-procedure, is developed to solve the problem of
classifying d-dimensional objects into q >= 2 classes. The procedure is
completely nonparametric; it uses q-dimensional depth plots and a very
efficient algorithm for discrimination analysis in the depth space [0,1]^q.
Specifically, the depth i... | computer science |
5,564 | Optimal discovery with probabilistic expert advice: finite time analysis
and macroscopic optimality | cs.LG | We consider an original problem that arises from the issue of security
analysis of a power system and that we name optimal discovery with
probabilistic expert advice. We address it with an algorithm based on the
optimistic paradigm and on the Good-Turing missing mass estimator. We prove two
different regret bounds on t... | computer science |
5,565 | Generalization Bounds for Metric and Similarity Learning | cs.LG | Recently, metric learning and similarity learning have attracted a large
amount of interest. Many models and optimisation algorithms have been proposed.
However, there is relatively little work on the generalization analysis of such
methods. In this paper, we derive novel generalization bounds of metric and
similarity ... | computer science |
5,566 | Bellman Error Based Feature Generation using Random Projections on
Sparse Spaces | cs.LG | We address the problem of automatic generation of features for value function
approximation. Bellman Error Basis Functions (BEBFs) have been shown to improve
the error of policy evaluation with function approximation, with a convergence
rate similar to that of value iteration. We propose a simple, fast and robust
algor... | computer science |
5,567 | Second-Order Non-Stationary Online Learning for Regression | cs.LG | The goal of a learner, in standard online learning, is to have the cumulative
loss not much larger compared with the best-performing function from some fixed
class. Numerous algorithms were shown to have this gap arbitrarily close to
zero, compared with the best function that is chosen off-line. Nevertheless,
many real... | computer science |
5,568 | One-Class Support Measure Machines for Group Anomaly Detection | stat.ML | We propose one-class support measure machines (OCSMMs) for group anomaly
detection which aims at recognizing anomalous aggregate behaviors of data
points. The OCSMMs generalize well-known one-class support vector machines
(OCSVMs) to a space of probability measures. By formulating the problem as
quantile estimation on ... | computer science |
5,569 | Top-down particle filtering for Bayesian decision trees | stat.ML | Decision tree learning is a popular approach for classification and
regression in machine learning and statistics, and Bayesian
formulations---which introduce a prior distribution over decision trees, and
formulate learning as posterior inference given data---have been shown to
produce competitive performance. Unlike c... | computer science |
5,570 | Bayesian Compressed Regression | stat.ML | As an alternative to variable selection or shrinkage in high dimensional
regression, we propose to randomly compress the predictors prior to analysis.
This dramatically reduces storage and computational bottlenecks, performing
well when the predictors can be projected to a low dimensional linear subspace
with minimal l... | computer science |
5,571 | An Equivalence between the Lasso and Support Vector Machines | cs.LG | We investigate the relation of two fundamental tools in machine learning and
signal processing, that is the support vector machine (SVM) for classification,
and the Lasso technique used in regression. We show that the resulting
optimization problems are equivalent, in the following sense. Given any
instance of an $\ell... | computer science |
5,572 | Classification with Asymmetric Label Noise: Consistency and Maximal
Denoising | stat.ML | In many real-world classification problems, the labels of training examples
are randomly corrupted. Most previous theoretical work on classification with
label noise assumes that the two classes are separable, that the label noise is
independent of the true class label, or that the noise proportions for each
class are ... | computer science |
5,573 | Large-Margin Metric Learning for Partitioning Problems | cs.LG | In this paper, we consider unsupervised partitioning problems, such as
clustering, image segmentation, video segmentation and other change-point
detection problems. We focus on partitioning problems based explicitly or
implicitly on the minimization of Euclidean distortions, which include
mean-based change-point detect... | computer science |
5,574 | Complex Support Vector Machines for Regression and Quaternary
Classification | cs.LG | The paper presents a new framework for complex Support Vector Regression as
well as Support Vector Machines for quaternary classification. The method
exploits the notion of widely linear estimation to model the input-out relation
for complex-valued data and considers two cases: a) the complex data are split
into their ... | computer science |
5,575 | Linear NDCG and Pair-wise Loss | cs.LG | Linear NDCG is used for measuring the performance of the Web content quality
assessment in ECML/PKDD Discovery Challenge 2010. In this paper, we will prove
that the DCG error equals a new pair-wise loss. | computer science |
5,576 | Monte-Carlo utility estimates for Bayesian reinforcement learning | cs.LG | This paper introduces a set of algorithms for Monte-Carlo Bayesian
reinforcement learning. Firstly, Monte-Carlo estimation of upper bounds on the
Bayes-optimal value function is employed to construct an optimistic policy.
Secondly, gradient-based algorithms for approximate upper and lower bounds are
introduced. Finally... | computer science |
5,577 | Online Learning in Markov Decision Processes with Adversarially Chosen
Transition Probability Distributions | cs.LG | We study the problem of learning Markov decision processes with finite state
and action spaces when the transition probability distributions and loss
functions are chosen adversarially and are allowed to change with time. We
introduce an algorithm whose regret with respect to any policy in a comparison
class grows as t... | computer science |
5,578 | Ranking and combining multiple predictors without labeled data | stat.ML | In a broad range of classification and decision making problems, one is given
the advice or predictions of several classifiers, of unknown reliability, over
multiple questions or queries. This scenario is different from the standard
supervised setting, where each classifier accuracy can be assessed using
available labe... | computer science |
5,579 | Topic Discovery through Data Dependent and Random Projections | stat.ML | We present algorithms for topic modeling based on the geometry of
cross-document word-frequency patterns. This perspective gains significance
under the so called separability condition. This is a condition on existence of
novel-words that are unique to each topic. We present a suite of highly
efficient algorithms based... | computer science |
5,580 | Margins, Shrinkage, and Boosting | cs.LG | This manuscript shows that AdaBoost and its immediate variants can produce
approximate maximum margin classifiers simply by scaling step size choices with
a fixed small constant. In this way, when the unscaled step size is an optimal
choice, these results provide guarantees for Friedman's empirically successful
"shrink... | computer science |
5,581 | Marginal Likelihoods for Distributed Parameter Estimation of Gaussian
Graphical Models | stat.ML | We consider distributed estimation of the inverse covariance matrix, also
called the concentration or precision matrix, in Gaussian graphical models.
Traditional centralized estimation often requires global inference of the
covariance matrix, which can be computationally intensive in large dimensions.
Approximate infer... | computer science |
5,582 | On Learnability, Complexity and Stability | stat.ML | We consider the fundamental question of learnability of a hypotheses class in
the supervised learning setting and in the general learning setting introduced
by Vladimir Vapnik. We survey classic results characterizing learnability in
term of suitable notions of complexity, as well as more recent results that
establish ... | computer science |
5,583 | On Sparsity Inducing Regularization Methods for Machine Learning | cs.LG | During the past years there has been an explosion of interest in learning
methods based on sparsity regularization. In this paper, we discuss a general
class of such methods, in which the regularizer can be expressed as the
composition of a convex function $\omega$ with a linear function. This setting
includes several ... | computer science |
5,584 | Exploiting correlation and budget constraints in Bayesian multi-armed
bandit optimization | stat.ML | We address the problem of finding the maximizer of a nonlinear smooth
function, that can only be evaluated point-wise, subject to constraints on the
number of permitted function evaluations. This problem is also known as
fixed-budget best arm identification in the multi-armed bandit literature. We
introduce a Bayesian ... | computer science |
5,585 | Sequential testing over multiple stages and performance analysis of data
fusion | stat.ML | We describe a methodology for modeling the performance of decision-level data
fusion between different sensor configurations, implemented as part of the
JIEDDO Analytic Decision Engine (JADE). We first discuss a Bayesian network
formulation of classical probabilistic data fusion, which allows elementary
fusion structur... | computer science |
5,586 | Efficiently Using Second Order Information in Large l1 Regularization
Problems | stat.ML | We propose a novel general algorithm LHAC that efficiently uses second-order
information to train a class of large-scale l1-regularized problems. Our method
executes cheap iterations while achieving fast local convergence rate by
exploiting the special structure of a low-rank matrix, constructed via
quasi-Newton approx... | computer science |
5,587 | ABC Reinforcement Learning | stat.ML | This paper introduces a simple, general framework for likelihood-free
Bayesian reinforcement learning, through Approximate Bayesian Computation
(ABC). The main advantage is that we only require a prior distribution on a
class of simulators (generative models). This is useful in domains where an
analytical probabilistic... | computer science |
5,588 | Relevance As a Metric for Evaluating Machine Learning Algorithms | stat.ML | In machine learning, the choice of a learning algorithm that is suitable for
the application domain is critical. The performance metric used to compare
different algorithms must also reflect the concerns of users in the application
domain under consideration. In this work, we propose a novel probability-based
performan... | computer science |
5,589 | Confidence-constrained joint sparsity recovery under the Poisson noise
model | stat.ML | Our work is focused on the joint sparsity recovery problem where the common
sparsity pattern is corrupted by Poisson noise. We formulate the
confidence-constrained optimization problem in both least squares (LS) and
maximum likelihood (ML) frameworks and study the conditions for perfect
reconstruction of the original r... | computer science |
5,590 | Convergence of Nearest Neighbor Pattern Classification with Selective
Sampling | cs.LG | In the panoply of pattern classification techniques, few enjoy the intuitive
appeal and simplicity of the nearest neighbor rule: given a set of samples in
some metric domain space whose value under some function is known, we estimate
the function anywhere in the domain by giving the value of the nearest sample
per the ... | computer science |
5,591 | Enhancements of Multi-class Support Vector Machine Construction from
Binary Learners using Generalization Performance | cs.LG | We propose several novel methods for enhancing the multi-class SVMs by
applying the generalization performance of binary classifiers as the core idea.
This concept will be applied on the existing algorithms, i.e., the Decision
Directed Acyclic Graph (DDAG), the Adaptive Directed Acyclic Graphs (ADAG), and
Max Wins. Alt... | computer science |
5,592 | Temporal Autoencoding Improves Generative Models of Time Series | stat.ML | Restricted Boltzmann Machines (RBMs) are generative models which can learn
useful representations from samples of a dataset in an unsupervised fashion.
They have been widely employed as an unsupervised pre-training method in
machine learning. RBMs have been modified to model time series in two main
ways: The Temporal R... | computer science |
5,593 | Multiple Instance Learning with Bag Dissimilarities | stat.ML | Multiple instance learning (MIL) is concerned with learning from sets (bags)
of objects (instances), where the individual instance labels are ambiguous. In
this setting, supervised learning cannot be applied directly. Often,
specialized MIL methods learn by making additional assumptions about the
relationship of the ba... | computer science |
5,594 | One-class Collaborative Filtering with Random Graphs: Annotated Version | stat.ML | The bane of one-class collaborative filtering is interpreting and modelling
the latent signal from the missing class. In this paper we present a novel
Bayesian generative model for implicit collaborative filtering. It forms a core
component of the Xbox Live architecture, and unlike previous approaches,
delineates the o... | computer science |
5,595 | Generative Multiple-Instance Learning Models For Quantitative
Electromyography | cs.LG | We present a comprehensive study of the use of generative modeling approaches
for Multiple-Instance Learning (MIL) problems. In MIL a learner receives
training instances grouped together into bags with labels for the bags only
(which might not be correct for the comprised instances). Our work was
motivated by the task ... | computer science |
5,596 | The Bregman Variational Dual-Tree Framework | cs.LG | Graph-based methods provide a powerful tool set for many non-parametric
frameworks in Machine Learning. In general, the memory and computational
complexity of these methods is quadratic in the number of examples in the data
which makes them quickly infeasible for moderate to large scale datasets. A
significant effort t... | computer science |
5,597 | Hinge-loss Markov Random Fields: Convex Inference for Structured
Prediction | cs.LG | Graphical models for structured domains are powerful tools, but the
computational complexities of combinatorial prediction spaces can force
restrictions on models, or require approximate inference in order to be
tractable. Instead of working in a combinatorial space, we use hinge-loss
Markov random fields (HL-MRFs), an... | computer science |
5,598 | High-dimensional Joint Sparsity Random Effects Model for Multi-task
Learning | cs.LG | Joint sparsity regularization in multi-task learning has attracted much
attention in recent years. The traditional convex formulation employs the group
Lasso relaxation to achieve joint sparsity across tasks. Although this approach
leads to a simple convex formulation, it suffers from several issues due to the
loosenes... | computer science |
5,599 | Boosting in the presence of label noise | cs.LG | Boosting is known to be sensitive to label noise. We studied two approaches
to improve AdaBoost's robustness against labelling errors. One is to employ a
label-noise robust classifier as a base learner, while the other is to modify
the AdaBoost algorithm to be more robust. Empirical evaluation shows that a
committee of... | computer science |
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