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5,300 | Staged Mixture Modelling and Boosting | cs.LG | In this paper, we introduce and evaluate a data-driven staged mixture
modeling technique for building density, regression, and classification models.
Our basic approach is to sequentially add components to a finite mixture model
using the structural expectation maximization (SEM) algorithm. We show that our
technique i... | computer science |
5,301 | Bayesian Network Classifiers in a High Dimensional Framework | cs.LG | We present a growing dimension asymptotic formalism. The perspective in this
paper is classification theory and we show that it can accommodate
probabilistic networks classifiers, including naive Bayes model and its
augmented version. When represented as a Bayesian network these classifiers
have an important advantage:... | computer science |
5,302 | Advances in Boosting (Invited Talk) | cs.LG | Boosting is a general method of generating many simple classification rules
and combining them into a single, highly accurate rule. In this talk, I will
review the AdaBoost boosting algorithm and some of its underlying theory, and
then look at how this theory has helped us to face some of the challenges of
applying Ada... | computer science |
5,303 | Reinforcement Learning with Partially Known World Dynamics | cs.LG | Reinforcement learning would enjoy better success on real-world problems if
domain knowledge could be imparted to the algorithm by the modelers. Most
problems have both hidden state and unknown dynamics. Partially observable
Markov decision processes (POMDPs) allow for the modeling of both.
Unfortunately, they do not p... | computer science |
5,304 | Unsupervised Active Learning in Large Domains | cs.LG | Active learning is a powerful approach to analyzing data effectively. We show
that the feasibility of active learning depends crucially on the choice of
measure with respect to which the query is being optimized. The standard
information gain, for example, does not permit an accurate evaluation with a
small committee, ... | computer science |
5,305 | A New Class of Upper Bounds on the Log Partition Function | cs.LG | Bounds on the log partition function are important in a variety of contexts,
including approximate inference, model fitting, decision theory, and large
deviations analysis. We introduce a new class of upper bounds on the log
partition function, based on convex combinations of distributions in the
exponential domain, th... | computer science |
5,306 | The Sum-over-Forests density index: identifying dense regions in a graph | cs.LG | This work introduces a novel nonparametric density index defined on graphs,
the Sum-over-Forests (SoF) density index. It is based on a clear and intuitive
idea: high-density regions in a graph are characterized by the fact that they
contain a large amount of low-cost trees with high outdegrees while low-density
regions... | computer science |
5,307 | Dynamical Models and Tracking Regret in Online Convex Programming | stat.ML | This paper describes a new online convex optimization method which
incorporates a family of candidate dynamical models and establishes novel
tracking regret bounds that scale with the comparator's deviation from the best
dynamical model in this family. Previous online optimization methods are
designed to have a total a... | computer science |
5,308 | Linear Bandits in High Dimension and Recommendation Systems | cs.LG | A large number of online services provide automated recommendations to help
users to navigate through a large collection of items. New items (products,
videos, songs, advertisements) are suggested on the basis of the user's past
history and --when available-- her demographic profile. Recommendations have to
satisfy the... | computer science |
5,309 | Bayesian Optimization in a Billion Dimensions via Random Embeddings | stat.ML | Bayesian optimization techniques have been successfully applied to robotics,
planning, sensor placement, recommendation, advertising, intelligent user
interfaces and automatic algorithm configuration. Despite these successes, the
approach is restricted to problems of moderate dimension, and several workshops
on Bayesia... | computer science |
5,310 | Heteroscedastic Relevance Vector Machine | stat.ML | In this work we propose a heteroscedastic generalization to RVM, a fast
Bayesian framework for regression, based on some recent similar works. We use
variational approximation and expectation propagation to tackle the problem.
The work is still under progress and we are examining the results and comparing
with the prev... | computer science |
5,311 | Domain Generalization via Invariant Feature Representation | stat.ML | This paper investigates domain generalization: How to take knowledge acquired
from an arbitrary number of related domains and apply it to previously unseen
domains? We propose Domain-Invariant Component Analysis (DICA), a kernel-based
optimization algorithm that learns an invariant transformation by minimizing
the diss... | computer science |
5,312 | Discovering Multiple Constraints that are Frequently Approximately
Satisfied | cs.LG | Some high-dimensional data.sets can be modelled by assuming that there are
many different linear constraints, each of which is Frequently Approximately
Satisfied (FAS) by the data. The probability of a data vector under the model
is then proportional to the product of the probabilities of its constraint
violations. We ... | computer science |
5,313 | Classifier Learning with Supervised Marginal Likelihood | cs.LG | It has been argued that in supervised classification tasks, in practice it
may be more sensible to perform model selection with respect to some more
focused model selection score, like the supervised (conditional) marginal
likelihood, than with respect to the standard marginal likelihood criterion.
However, for most Ba... | computer science |
5,314 | Iterative Markov Chain Monte Carlo Computation of Reference Priors and
Minimax Risk | cs.LG | We present an iterative Markov chainMonte Carlo algorithm for
computingreference priors and minimax risk forgeneral parametric families.
Ourapproach uses MCMC techniques based onthe Blahut-Arimoto algorithm
forcomputing channel capacity ininformation theory. We give astatistical
analysis of the algorithm,bounding the n... | computer science |
5,315 | Cross-covariance modelling via DAGs with hidden variables | cs.LG | DAG models with hidden variables present many difficulties that are not
present when all nodes are observed. In particular, fully observed DAG models
are identified and correspond to well-defined sets ofdistributions, whereas
this is not true if nodes are unobserved. Inthis paper we characterize exactly
the set of dist... | computer science |
5,316 | Functional Regularized Least Squares Classi cation with Operator-valued
Kernels | cs.LG | Although operator-valued kernels have recently received increasing interest
in various machine learning and functional data analysis problems such as
multi-task learning or functional regression, little attention has been paid to
the understanding of their associated feature spaces. In this paper, we explore
the potent... | computer science |
5,317 | Multiple functional regression with both discrete and continuous
covariates | stat.ML | In this paper we present a nonparametric method for extending functional
regression methodology to the situation where more than one functional
covariate is used to predict a functional response. Borrowing the idea from
Kadri et al. (2010a), the method, which support mixed discrete and continuous
explanatory variables,... | computer science |
5,318 | Matrix Approximation under Local Low-Rank Assumption | cs.LG | Matrix approximation is a common tool in machine learning for building
accurate prediction models for recommendation systems, text mining, and
computer vision. A prevalent assumption in constructing matrix approximations
is that the partially observed matrix is of low-rank. We propose a new matrix
approximation model w... | computer science |
5,319 | Joint Training Deep Boltzmann Machines for Classification | stat.ML | We introduce a new method for training deep Boltzmann machines jointly. Prior
methods of training DBMs require an initial learning pass that trains the model
greedily, one layer at a time, or do not perform well on classification tasks.
In our approach, we train all layers of the DBM simultaneously, using a novel
train... | computer science |
5,320 | Variational Relevance Vector Machines | cs.LG | The Support Vector Machine (SVM) of Vapnik (1998) has become widely
established as one of the leading approaches to pattern recognition and machine
learning. It expresses predictions in terms of a linear combination of kernel
functions centred on a subset of the training data, known as support vectors.
Despite its wi... | computer science |
5,321 | Bayesian Classification and Feature Selection from Finite Data Sets | cs.LG | Feature selection aims to select the smallest subset of features for a
specified level of performance. The optimal achievable classification
performance on a feature subset is summarized by its Receiver Operating Curve
(ROC). When infinite data is available, the Neyman- Pearson (NP) design
procedure provides the most e... | computer science |
5,322 | Experiments with Random Projection | cs.LG | Recent theoretical work has identified random projection as a promising
dimensionality reduction technique for learning mixtures of Gausians. Here we
summarize these results and illustrate them by a wide variety of experiments on
synthetic and real data. | computer science |
5,323 | A Two-round Variant of EM for Gaussian Mixtures | cs.LG | Given a set of possible models (e.g., Bayesian network structures) and a data
sample, in the unsupervised model selection problem the task is to choose the
most accurate model with respect to the domain joint probability distribution.
In contrast to this, in supervised model selection it is a priori known that
the chos... | computer science |
5,324 | Minimum Message Length Clustering Using Gibbs Sampling | cs.LG | The K-Mean and EM algorithms are popular in clustering and mixture modeling,
due to their simplicity and ease of implementation. However, they have several
significant limitations. Both coverage to a local optimum of their respective
objective functions (ignoring the uncertainty in the model space), require the
apriori... | computer science |
5,325 | Feature Selection and Dualities in Maximum Entropy Discrimination | cs.LG | Incorporating feature selection into a classification or regression method
often carries a number of advantages. In this paper we formalize feature
selection specifically from a discriminative perspective of improving
classification/regression accuracy. The feature selection method is developed
as an extension to the r... | computer science |
5,326 | Combining Feature and Prototype Pruning by Uncertainty Minimization | cs.LG | We focus in this paper on dataset reduction techniques for use in k-nearest
neighbor classification. In such a context, feature and prototype selections
have always been independently treated by the standard storage reduction
algorithms. While this certifying is theoretically justified by the fact that
each subproblem ... | computer science |
5,327 | An Uncertainty Framework for Classification | cs.LG | We define a generalized likelihood function based on uncertainty measures and
show that maximizing such a likelihood function for different measures induces
different types of classifiers. In the probabilistic framework, we obtain
classifiers that optimize the cross-entropy function. In the possibilistic
framework, we ... | computer science |
5,328 | Efficient Sample Reuse in Policy Gradients with Parameter-based
Exploration | cs.LG | The policy gradient approach is a flexible and powerful reinforcement
learning method particularly for problems with continuous actions such as robot
control. A common challenge in this scenario is how to reduce the variance of
policy gradient estimates for reliable policy updates. In this paper, we
combine the followi... | computer science |
5,329 | Latent Relation Representations for Universal Schemas | cs.LG | Traditional relation extraction predicts relations within some fixed and
finite target schema. Machine learning approaches to this task require either
manual annotation or, in the case of distant supervision, existing structured
sources of the same schema. The need for existing datasets can be avoided by
using a univer... | computer science |
5,330 | Piecewise Linear Multilayer Perceptrons and Dropout | stat.ML | We propose a new type of hidden layer for a multilayer perceptron, and
demonstrate that it obtains the best reported performance for an MLP on the
MNIST dataset. | computer science |
5,331 | Active Learning on Trees and Graphs | cs.LG | We investigate the problem of active learning on a given tree whose nodes are
assigned binary labels in an adversarial way. Inspired by recent results by
Guillory and Bilmes, we characterize (up to constant factors) the optimal
placement of queries so to minimize the mistakes made on the non-queried nodes.
Our query se... | computer science |
5,332 | Properties of the Least Squares Temporal Difference learning algorithm | stat.ML | This paper presents four different ways of looking at the well-known Least
Squares Temporal Differences (LSTD) algorithm for computing the value function
of a Markov Reward Process, each of them leading to different insights: the
operator-theory approach via the Galerkin method, the statistical approach via
instrumenta... | computer science |
5,333 | Online Learning with Pairwise Loss Functions | stat.ML | Efficient online learning with pairwise loss functions is a crucial component
in building large-scale learning system that maximizes the area under the
Receiver Operator Characteristic (ROC) curve. In this paper we investigate the
generalization performance of online learning algorithms with pairwise loss
functions. We... | computer science |
5,334 | Regularization and nonlinearities for neural language models: when are
they needed? | stat.ML | Neural language models (LMs) based on recurrent neural networks (RNN) are
some of the most successful word and character-level LMs. Why do they work so
well, in particular better than linear neural LMs? Possible explanations are
that RNNs have an implicitly better regularization or that RNNs have a higher
capacity for ... | computer science |
5,335 | Inferring Parameters and Structure of Latent Variable Models by
Variational Bayes | cs.LG | Current methods for learning graphical models with latent variables and a
fixed structure estimate optimal values for the model parameters. Whereas this
approach usually produces overfitting and suboptimal generalization
performance, carrying out the Bayesian program of computing the full posterior
distributions over t... | computer science |
5,336 | Relative Loss Bounds for On-line Density Estimation with the Exponential
Family of Distributions | cs.LG | We consider on-line density estimation with a parameterized density from the
exponential family. The on-line algorithm receives one example at a time and
maintains a parameter that is essentially an average of the past examples.
After receiving an example the algorithm incurs a loss which is the negative
log-likelihood... | computer science |
5,337 | Fast Learning from Sparse Data | cs.LG | We describe two techniques that significantly improve the running time of
several standard machine-learning algorithms when data is sparse. The first
technique is an algorithm that effeciently extracts one-way and two-way
counts--either real or expected-- from discrete data. Extracting such counts is
a fundamental step... | computer science |
5,338 | Parameter Priors for Directed Acyclic Graphical Models and the
Characterization of Several Probability Distributions | cs.LG | We show that the only parameter prior for complete Gaussian DAG models that
satisfies global parameter independence, complete model equivalence, and some
weak regularity assumptions, is the normal-Wishart distribution. Our analysis
is based on the following new characterization of the Wishart distribution: let
W be an ... | computer science |
5,339 | On Supervised Selection of Bayesian Networks | cs.LG | Given a set of possible models (e.g., Bayesian network structures) and a data
sample, in the unsupervised model selection problem the task is to choose the
most accurate model with respect to the domain joint probability distribution.
In contrast to this, in supervised model selection it is a priori known that
the chos... | computer science |
5,340 | Accelerating EM: An Empirical Study | cs.LG | Many applications require that we learn the parameters of a model from data.
EM is a method used to learn the parameters of probabilistic models for which
the data for some of the variables in the models is either missing or hidden.
There are instances in which this method is slow to converge. Therefore,
several accele... | computer science |
5,341 | Variational Learning in Mixed-State Dynamic Graphical Models | cs.LG | Many real-valued stochastic time-series are locally linear (Gassian), but
globally non-linear. For example, the trajectory of a human hand gesture can be
viewed as a linear dynamic system driven by a nonlinear dynamic system that
represents muscle actions. We present a mixed-state dynamic graphical model in
which a hid... | computer science |
5,342 | Approximate Learning in Complex Dynamic Bayesian Networks | cs.LG | In this paper we extend the work of Smith and Papamichail (1999) and present
fast approximate Bayesian algorithms for learning in complex scenarios where at
any time frame, the relationships between explanatory state space variables can
be described by a Bayesian network that evolve dynamically over time and the
observ... | computer science |
5,343 | On the Consistency of the Bootstrap Approach for Support Vector Machines
and Related Kernel Based Methods | stat.ML | It is shown that bootstrap approximations of support vector machines (SVMs)
based on a general convex and smooth loss function and on a general kernel are
consistent. This result is useful to approximate the unknown finite sample
distribution of SVMs by the bootstrap approach. | computer science |
5,344 | Learning by Transduction | cs.LG | We describe a method for predicting a classification of an object given
classifications of the objects in the training set, assuming that the pairs
object/classification are generated by an i.i.d. process from a continuous
probability distribution. Our method is a modification of Vapnik's
support-vector machine; its ma... | computer science |
5,345 | Graphical Models and Exponential Families | cs.LG | We provide a classification of graphical models according to their
representation as subfamilies of exponential families. Undirected graphical
models with no hidden variables are linear exponential families (LEFs),
directed acyclic graphical models and chain graphs with no hidden variables,
including Bayesian networks ... | computer science |
5,346 | Minimum Encoding Approaches for Predictive Modeling | cs.LG | We analyze differences between two information-theoretically motivated
approaches to statistical inference and model selection: the Minimum
Description Length (MDL) principle, and the Minimum Message Length (MML)
principle. Based on this analysis, we present two revised versions of MML: a
pointwise estimator which give... | computer science |
5,347 | Hierarchical Mixtures-of-Experts for Exponential Family Regression
Models with Generalized Linear Mean Functions: A Survey of Approximation and
Consistency Results | cs.LG | We investigate a class of hierarchical mixtures-of-experts (HME) models where
exponential family regression models with generalized linear mean functions of
the form psi(ga+fx^Tfgb) are mixed. Here psi(...) is the inverse link function.
Suppose the true response y follows an exponential family regression model with
mea... | computer science |
5,348 | Large Deviation Methods for Approximate Probabilistic Inference | cs.LG | We study two-layer belief networks of binary random variables in which the
conditional probabilities Pr[childlparents] depend monotonically on weighted
sums of the parents. In large networks where exact probabilistic inference is
intractable, we show how to compute upper and lower bounds on many
probabilities of intere... | computer science |
5,349 | Mixture Representations for Inference and Learning in Boltzmann Machines | cs.LG | Boltzmann machines are undirected graphical models with two-state stochastic
variables, in which the logarithms of the clique potentials are quadratic
functions of the node states. They have been widely studied in the neural
computing literature, although their practical applicability has been limited
by the difficulty... | computer science |
5,350 | An Experimental Comparison of Several Clustering and Initialization
Methods | cs.LG | We examine methods for clustering in high dimensions. In the first part of
the paper, we perform an experimental comparison between three batch clustering
algorithms: the Expectation-Maximization (EM) algorithm, a winner take all
version of the EM algorithm reminiscent of the K-means algorithm, and
model-based hierarch... | computer science |
5,351 | On the Geometry of Bayesian Graphical Models with Hidden Variables | cs.LG | In this paper we investigate the geometry of the likelihood of the unknown
parameters in a simple class of Bayesian directed graphs with hidden variables.
This enables us, before any numerical algorithms are employed, to obtain
certain insights in the nature of the unidentifiability inherent in such
models, the way pos... | computer science |
5,352 | Scaling the Indian Buffet Process via Submodular Maximization | stat.ML | Inference for latent feature models is inherently difficult as the inference
space grows exponentially with the size of the input data and number of latent
features. In this work, we use Kurihara & Welling (2008)'s
maximization-expectation framework to perform approximate MAP inference for
linear-Gaussian latent featur... | computer science |
5,353 | Advice-Efficient Prediction with Expert Advice | cs.LG | Advice-efficient prediction with expert advice (in analogy to label-efficient
prediction) is a variant of prediction with expert advice game, where on each
round of the game we are allowed to ask for advice of a limited number $M$ out
of $N$ experts. This setting is especially interesting when asking for advice
of ever... | computer science |
5,354 | Towards more accurate clustering method by using dynamic time warping | cs.LG | An intrinsic problem of classifiers based on machine learning (ML) methods is
that their learning time grows as the size and complexity of the training
dataset increases. For this reason, it is important to have efficient
computational methods and algorithms that can be applied on large datasets,
such that it is still ... | computer science |
5,355 | Unsupervised model-free representation learning | cs.LG | Numerous control and learning problems face the situation where sequences of
high-dimensional highly dependent data are available, but no or little feedback
is provided to the learner. To address this issue, we formulate the following
problem. Given a series of observations X_0,...,X_n coming from a large
(high-dimensi... | computer science |
5,356 | Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget | cs.LG | Can we make Bayesian posterior MCMC sampling more efficient when faced with
very large datasets? We argue that computing the likelihood for N datapoints in
the Metropolis-Hastings (MH) test to reach a single binary decision is
computationally inefficient. We introduce an approximate MH rule based on a
sequential hypoth... | computer science |
5,357 | Parallel Gaussian Process Optimization with Upper Confidence Bound and
Pure Exploration | cs.LG | In this paper, we consider the challenge of maximizing an unknown function f
for which evaluations are noisy and are acquired with high cost. An iterative
procedure uses the previous measures to actively select the next estimation of
f which is predicted to be the most useful. We focus on the case where the
function ca... | computer science |
5,358 | Optimal Stochastic Strongly Convex Optimization with a Logarithmic
Number of Projections | cs.LG | We consider stochastic strongly convex optimization with a complex inequality
constraint. This complex inequality constraint may lead to computationally
expensive projections in algorithmic iterations of the stochastic gradient
descent~(SGD) methods. To reduce the computation costs pertaining to the
projections, we pro... | computer science |
5,359 | Inverse Density as an Inverse Problem: The Fredholm Equation Approach | cs.LG | In this paper we address the problem of estimating the ratio $\frac{q}{p}$
where $p$ is a density function and $q$ is another density, or, more generally
an arbitrary function. Knowing or approximating this ratio is needed in various
problems of inference and integration, in particular, when one needs to average
a func... | computer science |
5,360 | Analytic Feature Selection for Support Vector Machines | cs.LG | Support vector machines (SVMs) rely on the inherent geometry of a data set to
classify training data. Because of this, we believe SVMs are an excellent
candidate to guide the development of an analytic feature selection algorithm,
as opposed to the more commonly used heuristic methods. We propose a
filter-based feature... | computer science |
5,361 | Prior-free and prior-dependent regret bounds for Thompson Sampling | stat.ML | We consider the stochastic multi-armed bandit problem with a prior
distribution on the reward distributions. We are interested in studying
prior-free and prior-dependent regret bounds, very much in the same spirit as
the usual distribution-free and distribution-dependent bounds for the
non-Bayesian stochastic bandit. B... | computer science |
5,362 | Locally linear representation for image clustering | cs.LG | It is a key to construct a similarity graph in graph-oriented subspace
learning and clustering. In a similarity graph, each vertex denotes a data
point and the edge weight represents the similarity between two points. There
are two popular schemes to construct a similarity graph, i.e., pairwise
distance based scheme an... | computer science |
5,363 | Learning Densities Conditional on Many Interacting Features | stat.ML | Learning a distribution conditional on a set of discrete-valued features is a
commonly encountered task. This becomes more challenging with a
high-dimensional feature set when there is the possibility of interaction
between the features. In addition, many frequently applied techniques consider
only prediction of the me... | computer science |
5,364 | Semi-Supervised Information-Maximization Clustering | cs.LG | Semi-supervised clustering aims to introduce prior knowledge in the decision
process of a clustering algorithm. In this paper, we propose a novel
semi-supervised clustering algorithm based on the information-maximization
principle. The proposed method is an extension of a previous unsupervised
information-maximization ... | computer science |
5,365 | RNADE: The real-valued neural autoregressive density-estimator | stat.ML | We introduce RNADE, a new model for joint density estimation of real-valued
vectors. Our model calculates the density of a datapoint as the product of
one-dimensional conditionals modeled using mixture density networks with shared
parameters. RNADE learns a distributed representation of the data, while having
a tractab... | computer science |
5,366 | Deep Learning using Linear Support Vector Machines | cs.LG | Recently, fully-connected and convolutional neural networks have been trained
to achieve state-of-the-art performance on a wide variety of tasks such as
speech recognition, image classification, natural language processing, and
bioinformatics. For classification tasks, most of these "deep learning" models
employ the so... | computer science |
5,367 | Learning from networked examples in a k-partite graph | cs.LG | Many machine learning algorithms are based on the assumption that training
examples are drawn independently. However, this assumption does not hold
anymore when learning from a networked sample where two or more training
examples may share common features. We propose an efficient weighting method
for learning from netw... | computer science |
5,368 | Prediction with Missing Data via Bayesian Additive Regression Trees | stat.ML | We present a method for incorporating missing data in non-parametric
statistical learning without the need for imputation. We focus on a tree-based
method, Bayesian Additive Regression Trees (BART), enhanced with "Missingness
Incorporated in Attributes," an approach recently proposed incorporating
missingness into deci... | computer science |
5,369 | Fast Gradient-Based Inference with Continuous Latent Variable Models in
Auxiliary Form | cs.LG | We propose a technique for increasing the efficiency of gradient-based
inference and learning in Bayesian networks with multiple layers of continuous
latent vari- ables. We show that, in many cases, it is possible to express such
models in an auxiliary form, where continuous latent variables are
conditionally determini... | computer science |
5,370 | $\propto$SVM for learning with label proportions | cs.LG | We study the problem of learning with label proportions in which the training
data is provided in groups and only the proportion of each class in each group
is known. We propose a new method called proportion-SVM, or $\propto$SVM, which
explicitly models the latent unknown instance labels together with the known
group ... | computer science |
5,371 | (More) Efficient Reinforcement Learning via Posterior Sampling | stat.ML | Most provably-efficient learning algorithms introduce optimism about
poorly-understood states and actions to encourage exploration. We study an
alternative approach for efficient exploration, posterior sampling for
reinforcement learning (PSRL). This algorithm proceeds in repeated episodes of
known duration. At the sta... | computer science |
5,372 | Bayesian Differential Privacy through Posterior Sampling | stat.ML | Differential privacy formalises privacy-preserving mechanisms that provide
access to a database. We pose the question of whether Bayesian inference itself
can be used directly to provide private access to data, with no modification.
The answer is affirmative: under certain conditions on the prior, sampling from
the pos... | computer science |
5,373 | Tight Lower Bound on the Probability of a Binomial Exceeding its
Expectation | cs.LG | We give the proof of a tight lower bound on the probability that a binomial
random variable exceeds its expected value. The inequality plays an important
role in a variety of contexts, including the analysis of relative deviation
bounds in learning theory and generalization bounds for unbounded loss
functions. | computer science |
5,374 | Loss-Proportional Subsampling for Subsequent ERM | cs.LG | We propose a sampling scheme suitable for reducing a data set prior to
selecting a hypothesis with minimum empirical risk. The sampling only considers
a subset of the ultimate (unknown) hypothesis set, but can nonetheless
guarantee that the final excess risk will compare favorably with utilizing the
entire original dat... | computer science |
5,375 | Logistic Tensor Factorization for Multi-Relational Data | stat.ML | Tensor factorizations have become increasingly popular approaches for various
learning tasks on structured data. In this work, we extend the RESCAL tensor
factorization, which has shown state-of-the-art results for multi-relational
learning, to account for the binary nature of adjacency tensors. We study the
improvemen... | computer science |
5,376 | DISCOMAX: A Proximity-Preserving Distance Correlation Maximization
Algorithm | cs.LG | In a regression setting we propose algorithms that reduce the dimensionality
of the features while simultaneously maximizing a statistical measure of
dependence known as distance correlation between the low-dimensional features
and a response variable. This helps in solving the prediction problem with a
low-dimensional... | computer science |
5,377 | Stochastic approximation for speeding up LSTD (and LSPI) | cs.LG | We propose a stochastic approximation (SA) based method with randomization of
samples for policy evaluation using the least squares temporal difference
(LSTD) algorithm. Our method results in an $O(d)$ improvement in complexity in
comparison to regular LSTD, where $d$ is the dimension of the data. We provide
convergenc... | computer science |
5,378 | Copula Mixed-Membership Stochastic Blockmodel for Intra-Subgroup
Correlations | cs.LG | The \emph{Mixed-Membership Stochastic Blockmodel (MMSB)} is a popular
framework for modeling social network relationships. It can fully exploit each
individual node's participation (or membership) in a social structure. Despite
its powerful representations, this model makes an assumption that the
distributions of relat... | computer science |
5,379 | Horizontal and Vertical Ensemble with Deep Representation for
Classification | cs.LG | Representation learning, especially which by using deep learning, has been
widely applied in classification. However, how to use limited size of labeled
data to achieve good classification performance with deep neural network, and
how can the learned features further improve classification remain indefinite.
In this pa... | computer science |
5,380 | A Convergence Theorem for the Graph Shift-type Algorithms | stat.ML | Graph Shift (GS) algorithms are recently focused as a promising approach for
discovering dense subgraphs in noisy data. However, there are no theoretical
foundations for proving the convergence of the GS Algorithm. In this paper, we
propose a generic theoretical framework consisting of three key GS components:
simplex ... | computer science |
5,381 | Learning Using Privileged Information: SVM+ and Weighted SVM | stat.ML | Prior knowledge can be used to improve predictive performance of learning
algorithms or reduce the amount of data required for training. The same goal is
pursued within the learning using privileged information paradigm which was
recently introduced by Vapnik et al. and is aimed at utilizing additional
information avai... | computer science |
5,382 | Sparse Inverse Covariance Matrix Estimation Using Quadratic
Approximation | cs.LG | The L1-regularized Gaussian maximum likelihood estimator (MLE) has been shown
to have strong statistical guarantees in recovering a sparse inverse covariance
matrix, or alternatively the underlying graph structure of a Gaussian Markov
Random Field, from very limited samples. We propose a novel algorithm for
solving the... | computer science |
5,383 | Spectral Experts for Estimating Mixtures of Linear Regressions | cs.LG | Discriminative latent-variable models are typically learned using EM or
gradient-based optimization, which suffer from local optima. In this paper, we
develop a new computationally efficient and provably consistent estimator for a
mixture of linear regressions, a simple instance of a discriminative
latent-variable mode... | computer science |
5,384 | Stability of Multi-Task Kernel Regression Algorithms | cs.LG | We study the stability properties of nonlinear multi-task regression in
reproducing Hilbert spaces with operator-valued kernels. Such kernels, a.k.a.
multi-task kernels, are appropriate for learning prob- lems with nonscalar
outputs like multi-task learning and structured out- put prediction. We show
that multi-task ke... | computer science |
5,385 | On Finding the Largest Mean Among Many | stat.ML | Sampling from distributions to find the one with the largest mean arises in a
broad range of applications, and it can be mathematically modeled as a
multi-armed bandit problem in which each distribution is associated with an
arm. This paper studies the sample complexity of identifying the best arm
(largest mean) in a m... | computer science |
5,386 | Bioclimating Modelling: A Machine Learning Perspective | cs.LG | Many machine learning (ML) approaches are widely used to generate bioclimatic
models for prediction of geographic range of organism as a function of climate.
Applications such as prediction of range shift in organism, range of invasive
species influenced by climate change are important parameters in understanding
the i... | computer science |
5,387 | Joint estimation of sparse multivariate regression and conditional
graphical models | stat.ML | Multivariate regression model is a natural generalization of the classical
univari- ate regression model for fitting multiple responses. In this paper, we
propose a high- dimensional multivariate conditional regression model for
constructing sparse estimates of the multivariate regression coefficient matrix
that accoun... | computer science |
5,388 | Class Proportion Estimation with Application to Multiclass Anomaly
Rejection | stat.ML | This work addresses two classification problems that fall under the heading
of domain adaptation, wherein the distributions of training and testing
examples differ. The first problem studied is that of class proportion
estimation, which is the problem of estimating the class proportions in an
unlabeled testing data set... | computer science |
5,389 | Deep Learning by Scattering | cs.LG | We introduce general scattering transforms as mathematical models of deep
neural networks with l2 pooling. Scattering networks iteratively apply complex
valued unitary operators, and the pooling is performed by a complex modulus. An
expected scattering defines a contractive representation of a high-dimensional
probabil... | computer science |
5,390 | Correlated random features for fast semi-supervised learning | stat.ML | This paper presents Correlated Nystrom Views (XNV), a fast semi-supervised
algorithm for regression and classification. The algorithm draws on two main
ideas. First, it generates two views consisting of computationally inexpensive
random features. Second, XNV applies multiview regression using Canonical
Correlation Ana... | computer science |
5,391 | Scaling Up Robust MDPs by Reinforcement Learning | cs.LG | We consider large-scale Markov decision processes (MDPs) with parameter
uncertainty, under the robust MDP paradigm. Previous studies showed that robust
MDPs, based on a minimax approach to handle uncertainty, can be solved using
dynamic programming for small to medium sized problems. However, due to the
"curse of dimen... | computer science |
5,392 | A Survey on Metric Learning for Feature Vectors and Structured Data | cs.LG | The need for appropriate ways to measure the distance or similarity between
data is ubiquitous in machine learning, pattern recognition and data mining,
but handcrafting such good metrics for specific problems is generally
difficult. This has led to the emergence of metric learning, which aims at
automatically learning... | computer science |
5,393 | Spectral Methods for Learning Multivariate Latent Tree Structure | cs.LG | This work considers the problem of learning the structure of multivariate
linear tree models, which include a variety of directed tree graphical models
with continuous, discrete, and mixed latent variables such as linear-Gaussian
models, hidden Markov models, Gaussian mixture models, and Markov evolutionary
trees. The ... | computer science |
5,394 | Multi-Instance Learning with Any Hypothesis Class | cs.LG | In the supervised learning setting termed Multiple-Instance Learning (MIL),
the examples are bags of instances, and the bag label is a function of the
labels of its instances. Typically, this function is the Boolean OR. The
learner observes a sample of bags and the bag labels, but not the instance
labels that determine... | computer science |
5,395 | Statistical Topic Models for Multi-Label Document Classification | stat.ML | Machine learning approaches to multi-label document classification have to
date largely relied on discriminative modeling techniques such as support
vector machines. A drawback of these approaches is that performance rapidly
drops off as the total number of labels and the number of labels per document
increase. This pr... | computer science |
5,396 | Unsupervised K-Nearest Neighbor Regression | stat.ML | In many scientific disciplines structures in high-dimensional data have to be
found, e.g., in stellar spectra, in genome data, or in face recognition tasks.
In this work we present a novel approach to non-linear dimensionality
reduction. It is based on fitting K-nearest neighbor regression to the
unsupervised regressio... | computer science |
5,397 | Trading USDCHF filtered by Gold dynamics via HMM coupling | stat.ML | We devise a USDCHF trading strategy using the dynamics of gold as a filter.
Our strategy involves modelling both USDCHF and gold using a coupled hidden
Markov model (CHMM). The observations will be indicators, RSI and CCI, which
will be used as triggers for our trading signals. Upon decoding the model in
each iteration... | computer science |
5,398 | High-Dimensional Regression with Gaussian Mixtures and Partially-Latent
Response Variables | cs.LG | In this work we address the problem of approximating high-dimensional data
with a low-dimensional representation. We make the following contributions. We
propose an inverse regression method which exchanges the roles of input and
response, such that the low-dimensional variable becomes the regressor, and
which is tract... | computer science |
5,399 | The algorithm of noisy k-means | stat.ML | In this note, we introduce a new algorithm to deal with finite dimensional
clustering with errors in variables. The design of this algorithm is based on
recent theoretical advances (see Loustau (2013a,b)) in statistical learning
with errors in variables. As the previous mentioned papers, the algorithm mixes
different t... | computer science |
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