Unnamed: 0 int64 0 41k | title stringlengths 4 274 | category stringlengths 5 18 | summary stringlengths 22 3.66k | theme stringclasses 8
values |
|---|---|---|---|---|
21,902 | Discriminative training for Convolved Multiple-Output Gaussian processes | stat.ML | Multi-output Gaussian processes (MOGP) are probability distributions over
vector-valued functions, and have been previously used for multi-output
regression and for multi-class classification. A less explored facet of the
multi-output Gaussian process is that it can be used as a generative model for
vector-valued rando... | computer science |
21,903 | Measuring the functional connectome "on-the-fly": towards a new control
signal for fMRI-based brain-computer interfaces | stat.ML | There has been an explosion of interest in functional Magnetic Resonance
Imaging (MRI) during the past two decades. Naturally, this has been accompanied
by many major advances in the understanding of the human connectome. These
advances have served to pose novel challenges as well as open new avenues for
research. One ... | computer science |
21,904 | Regularization Path of Cross-Validation Error Lower Bounds | stat.ML | Careful tuning of a regularization parameter is indispensable in many machine
learning tasks because it has a significant impact on generalization
performances. Nevertheless, current practice of regularization parameter tuning
is more of an art than a science, e.g., it is hard to tell how many grid-points
would be need... | computer science |
21,905 | Local and Global Inference for High Dimensional Nonparanormal Graphical
Models | stat.ML | This paper proposes a unified framework to quantify local and global
inferential uncertainty for high dimensional nonparanormal graphical models. In
particular, we consider the problems of testing the presence of a single edge
and constructing a uniform confidence subgraph. Due to the presence of unknown
marginal trans... | computer science |
21,906 | Distributed Gaussian Processes | stat.ML | To scale Gaussian processes (GPs) to large data sets we introduce the robust
Bayesian Committee Machine (rBCM), a practical and scalable product-of-experts
model for large-scale distributed GP regression. Unlike state-of-the-art sparse
GP approximations, the rBCM is conceptually simple and does not rely on
inducing or ... | computer science |
21,907 | Reconstruction in the Labeled Stochastic Block Model | stat.ML | The labeled stochastic block model is a random graph model representing
networks with community structure and interactions of multiple types. In its
simplest form, it consists of two communities of approximately equal size, and
the edges are drawn and labeled at random with probability depending on whether
their two en... | computer science |
21,908 | Dependent Matérn Processes for Multivariate Time Series | stat.ML | For the challenging task of modeling multivariate time series, we propose a
new class of models that use dependent Mat\'ern processes to capture the
underlying structure of data, explain their interdependencies, and predict
their unknown values. Although similar models have been proposed in the
econometric, statistics,... | computer science |
21,909 | Monte Carlo Planning method estimates planning horizons during
interactive social exchange | stat.ML | Reciprocating interactions represent a central feature of all human
exchanges. They have been the target of various recent experiments, with
healthy participants and psychiatric populations engaging as dyads in
multi-round exchanges such as a repeated trust task. Behaviour in such
exchanges involves complexities relate... | computer science |
21,910 | Fast and Memory-Efficient Significant Pattern Mining via Permutation
Testing | stat.ML | We present a novel algorithm, Westfall-Young light, for detecting patterns,
such as itemsets and subgraphs, which are statistically significantly enriched
in one of two classes. Our method corrects rigorously for multiple hypothesis
testing and correlations between patterns through the Westfall-Young
permutation proced... | computer science |
21,911 | Random Subspace Learning Approach to High-Dimensional Outliers Detection | stat.ML | We introduce and develop a novel approach to outlier detection based on
adaptation of random subspace learning. Our proposed method handles both
high-dimension low-sample size and traditional low-dimensional high-sample size
datasets. Essentially, we avoid the computational bottleneck of techniques like
minimum covaria... | computer science |
21,912 | Clustering and Inference From Pairwise Comparisons | stat.ML | Given a set of pairwise comparisons, the classical ranking problem computes a
single ranking that best represents the preferences of all users. In this
paper, we study the problem of inferring individual preferences, arising in the
context of making personalized recommendations. In particular, we assume that
there are ... | computer science |
21,913 | Predictive Entropy Search for Bayesian Optimization with Unknown
Constraints | stat.ML | Unknown constraints arise in many types of expensive black-box optimization
problems. Several methods have been proposed recently for performing Bayesian
optimization with constraints, based on the expected improvement (EI)
heuristic. However, EI can lead to pathologies when used with constraints. For
example, in the c... | computer science |
21,914 | Variational Optimization of Annealing Schedules | stat.ML | Annealed importance sampling (AIS) is a common algorithm to estimate
partition functions of useful stochastic models. One important problem for
obtaining accurate AIS estimates is the selection of an annealing schedule.
Conventionally, an annealing schedule is often determined heuristically or is
simply set as a linear... | computer science |
21,915 | Probabilistic Backpropagation for Scalable Learning of Bayesian Neural
Networks | stat.ML | Large multilayer neural networks trained with backpropagation have recently
achieved state-of-the-art results in a wide range of problems. However, using
backprop for neural net learning still has some disadvantages, e.g., having to
tune a large number of hyperparameters to the data, lack of calibrated
probabilistic pr... | computer science |
21,916 | Scalable Bayesian Optimization Using Deep Neural Networks | stat.ML | Bayesian optimization is an effective methodology for the global optimization
of functions with expensive evaluations. It relies on querying a distribution
over functions defined by a relatively cheap surrogate model. An accurate model
for this distribution over functions is critical to the effectiveness of the
approac... | computer science |
21,917 | 1-Bit Matrix Completion under Exact Low-Rank Constraint | stat.ML | We consider the problem of noisy 1-bit matrix completion under an exact rank
constraint on the true underlying matrix $M^*$. Instead of observing a subset
of the noisy continuous-valued entries of a matrix $M^*$, we observe a subset
of noisy 1-bit (or binary) measurements generated according to a probabilistic
model. W... | computer science |
21,918 | On Convolutional Approximations to Linear Dimensionality Reduction
Operators for Large Scale Data Processing | stat.ML | In this paper, we examine the problem of approximating a general linear
dimensionality reduction (LDR) operator, represented as a matrix $A \in
\mathbb{R}^{m \times n}$ with $m < n$, by a partial circulant matrix with rows
related by circular shifts. Partial circulant matrices admit fast
implementations via Fourier tra... | computer science |
21,919 | A Note on the Kullback-Leibler Divergence for the von Mises-Fisher
distribution | stat.ML | We present a derivation of the Kullback Leibler (KL)-Divergence (also known
as Relative Entropy) for the von Mises Fisher (VMF) Distribution in
$d$-dimensions. | computer science |
21,920 | Sparse Multivariate Factor Regression | stat.ML | We consider the problem of multivariate regression in a setting where the
relevant predictors could be shared among different responses. We propose an
algorithm which decomposes the coefficient matrix into the product of a long
matrix and a wide matrix, with an elastic net penalty on the former and an
$\ell_1$ penalty ... | computer science |
21,921 | Matrix Completion with Noisy Entries and Outliers | stat.ML | This paper considers the problem of matrix completion when the observed
entries are noisy and contain outliers. It begins with introducing a new
optimization criterion for which the recovered matrix is defined as its
solution. This criterion uses the celebrated Huber function from the robust
statistics literature to do... | computer science |
21,922 | Statistical Limits of Convex Relaxations | stat.ML | Many high dimensional sparse learning problems are formulated as nonconvex
optimization. A popular approach to solve these nonconvex optimization problems
is through convex relaxations such as linear and semidefinite programming. In
this paper, we study the statistical limits of convex relaxations.
Particularly, we con... | computer science |
21,923 | Local Expectation Gradients for Doubly Stochastic Variational Inference | stat.ML | We introduce local expectation gradients which is a general purpose
stochastic variational inference algorithm for constructing stochastic
gradients through sampling from the variational distribution. This algorithm
divides the problem of estimating the stochastic gradients over multiple
variational parameters into sma... | computer science |
21,924 | Latent Gaussian Processes for Distribution Estimation of Multivariate
Categorical Data | stat.ML | Multivariate categorical data occur in many applications of machine learning.
One of the main difficulties with these vectors of categorical variables is
sparsity. The number of possible observations grows exponentially with vector
length, but dataset diversity might be poor in comparison. Recent models have
gained sig... | computer science |
21,925 | Improving the Gaussian Process Sparse Spectrum Approximation by
Representing Uncertainty in Frequency Inputs | stat.ML | Standard sparse pseudo-input approximations to the Gaussian process (GP)
cannot handle complex functions well. Sparse spectrum alternatives attempt to
answer this but are known to over-fit. We suggest the use of variational
inference for the sparse spectrum approximation to avoid both issues. We model
the covariance fu... | computer science |
21,926 | Graphical Exponential Screening | stat.ML | In high dimensions we propose and analyze an aggregation estimator of the
precision matrix for Gaussian graphical models. This estimator, called
graphical Exponential Screening (gES), linearly combines a suitable set of
individual estimators with different underlying graphs, and balances the
estimation error and sparsi... | computer science |
21,927 | Novel Bernstein-like Concentration Inequalities for the Missing Mass | stat.ML | We are concerned with obtaining novel concentration inequalities for the
missing mass, i.e. the total probability mass of the outcomes not observed in
the sample. We not only derive - for the first time - distribution-free
Bernstein-like deviation bounds with sublinear exponents in deviation size for
missing mass, but ... | computer science |
21,928 | Learning the Structure for Structured Sparsity | stat.ML | Structured sparsity has recently emerged in statistics, machine learning and
signal processing as a promising paradigm for learning in high-dimensional
settings. All existing methods for learning under the assumption of structured
sparsity rely on prior knowledge on how to weight (or how to penalize)
individual subsets... | computer science |
21,929 | Non-parametric Bayesian Models of Response Function in Dynamic Image
Sequences | stat.ML | Estimation of response functions is an important task in dynamic medical
imaging. This task arises for example in dynamic renal scintigraphy, where
impulse response or retention functions are estimated, or in functional
magnetic resonance imaging where hemodynamic response functions are required.
These functions can no... | computer science |
21,930 | A Bennett Inequality for the Missing Mass | stat.ML | Novel concentration inequalities are obtained for the missing mass, i.e. the
total probability mass of the outcomes not observed in the sample. We derive
distribution-free deviation bounds with sublinear exponents in deviation size
for missing mass and improve the results of Berend and Kontorovich (2013) and
Yari Saeed... | computer science |
21,931 | Indian Buffet process for model selection in convolved multiple-output
Gaussian processes | stat.ML | Multi-output Gaussian processes have received increasing attention during the
last few years as a natural mechanism to extend the powerful flexibility of
Gaussian processes to the setup of multiple output variables. The key point
here is the ability to design kernel functions that allow exploiting the
correlations betw... | computer science |
21,932 | Efficient Online Minimization for Low-Rank Subspace Clustering | stat.ML | Low-rank representation~(LRR) has been a significant method for segmenting
data that are generated from a union of subspaces. It is, however, known that
solving the LRR program is challenging in terms of time complexity and memory
footprint, in that the size of the nuclear norm regularized matrix is
$n$-by-$n$ (where $... | computer science |
21,933 | A Parzen-based distance between probability measures as an alternative
of summary statistics in Approximate Bayesian Computation | stat.ML | Approximate Bayesian Computation (ABC) are likelihood-free Monte Carlo
methods. ABC methods use a comparison between simulated data, using different
parameters drew from a prior distribution, and observed data. This comparison
process is based on computing a distance between the summary statistics from
the simulated da... | computer science |
21,934 | Two Methods For Wild Variational Inference | stat.ML | Variational inference provides a powerful tool for approximate probabilistic
in- ference on complex, structured models. Typical variational inference
methods, however, require to use inference networks with computationally
tractable proba- bility density functions. This largely limits the design and
implementation of v... | computer science |
21,935 | Transfer Learning via Latent Factor Modeling to Improve Prediction of
Surgical Complications | stat.ML | We aim to create a framework for transfer learning using latent factor models
to learn the dependence structure between a larger source dataset and a target
dataset. The methodology is motivated by our goal of building a risk-assessment
model for surgery patients, using both institutional and national surgical
outcomes... | computer science |
21,936 | Parallel Chromatic MCMC with Spatial Partitioning | stat.ML | We introduce a novel approach for parallelizing MCMC inference in models with
spatially determined conditional independence relationships, for which existing
techniques exploiting graphical model structure are not applicable. Our
approach is motivated by a model of seismic events and signals, where events
detected in d... | computer science |
21,937 | Whiteout: Gaussian Adaptive Noise Regularization in FeedForward Neural
Networks | stat.ML | Noise injection (NI) is an approach to mitigate over-fitting in feedforward
neural networks (NNs). The Bernoulli NI procedure as implemented in dropout and
shakeout has connections with $l_1$ and $l_2$ regularization on the NN model
parameters and demonstrates the efficiency and feasibility of NI in
regularizing NNs. W... | computer science |
21,938 | Supervised topic models for clinical interpretability | stat.ML | Supervised topic models can help clinical researchers find interpretable
cooccurence patterns in count data that are relevant for diagnostics. However,
standard formulations of supervised Latent Dirichlet Allocation have two
problems. First, when documents have many more words than labels, the influence
of the labels w... | computer science |
21,939 | Smoothing Effects of Bagging: Von Mises Expansions of Bagged Statistical
Functionals | stat.ML | Bagging is a device intended for reducing the prediction error of learning
algorithms. In its simplest form, bagging draws bootstrap samples from the
training sample, applies the learning algorithm to each bootstrap sample, and
then averages the resulting prediction rules.
We extend the definition of bagging from sta... | computer science |
21,940 | Characterizing the maximum parameter of the total-variation denoising
through the pseudo-inverse of the divergence | stat.ML | We focus on the maximum regularization parameter for anisotropic
total-variation denoising. It corresponds to the minimum value of the
regularization parameter above which the solution remains constant. While this
value is well know for the Lasso, such a critical value has not been
investigated in details for the total... | computer science |
21,941 | A New Spectral Method for Latent Variable Models | stat.ML | This paper presents an algorithm for the unsupervised learning of latent
variable models from unlabeled sets of data. We base our technique on spectral
decomposition, providing a technique that proves to be robust both in theory
and in practice. We also describe how to use this algorithm to learn the
parameters of two ... | computer science |
21,942 | Monte Carlo Structured SVI for Two-Level Non-Conjugate Models | stat.ML | The stochastic variational inference (SVI) paradigm, which combines
variational inference, natural gradients, and stochastic updates, was recently
proposed for large-scale data analysis in conjugate Bayesian models and
demonstrated to be effective in several problems. This paper studies a family
of Bayesian latent vari... | computer science |
21,943 | Robust Local Scaling using Conditional Quantiles of Graph Similarities | stat.ML | Spectral analysis of neighborhood graphs is one of the most widely used
techniques for exploratory data analysis, with applications ranging from
machine learning to social sciences. In such applications, it is typical to
first encode relationships between the data samples using an appropriate
similarity function. Popul... | computer science |
21,944 | Edge-exchangeable graphs and sparsity (NIPS 2016) | stat.ML | Many popular network models rely on the assumption of (vertex)
exchangeability, in which the distribution of the graph is invariant to
relabelings of the vertices. However, the Aldous-Hoover theorem guarantees that
these graphs are dense or empty with probability one, whereas many real-world
graphs are sparse. We prese... | computer science |
21,945 | Causal Discovery as Semi-Supervised Learning | stat.ML | In this short report, we discuss an approach to estimating causal graphs in
which indicators of causal influence between variables are treated as labels in
a machine learning formulation. Available data on the variables of interest are
used as "inputs" to estimate the labels. We frame the problem as one of
semi-supervi... | computer science |
21,946 | Optimal tuning for divide-and-conquer kernel ridge regression with
massive data | stat.ML | We propose a first data-driven tuning procedure for divide-and-conquer kernel
ridge regression (Zhang et al., 2015). While the proposed criterion is
computationally scalable for massive data sets, it is also shown to be
asymptotically optimal under mild conditions. The effectiveness of our method
is illustrated by exte... | computer science |
21,947 | Mixing Times and Structural Inference for Bernoulli Autoregressive
Processes | stat.ML | We introduce a novel multivariate random process producing Bernoulli outputs
per dimension, that can possibly formalize binary interactions in various
graphical structures and can be used to model opinion dynamics, epidemics,
financial and biological time series data, etc. We call this a Bernoulli
Autoregressive Proces... | computer science |
21,948 | Bayesian Optimization with Shape Constraints | stat.ML | In typical applications of Bayesian optimization, minimal assumptions are
made about the objective function being optimized. This is true even when
researchers have prior information about the shape of the function with respect
to one or more argument. We make the case that shape constraints are often
appropriate in at... | computer science |
21,949 | Optimal bandwidth estimation for a fast manifold learning algorithm to
detect circular structure in high-dimensional data | stat.ML | We provide a way to infer about existence of topological circularity in
high-dimensional data sets in $\mathbb{R}^d$ from its projection in
$\mathbb{R}^2$ obtained through a fast manifold learning map as a function of
the high-dimensional dataset $\mathbb{X}$ and a particular choice of a positive
real $\sigma$ known as... | computer science |
21,950 | Communication-efficient Distributed Estimation and Inference for
Transelliptical Graphical Models | stat.ML | We propose communication-efficient distributed estimation and inference
methods for the transelliptical graphical model, a semiparametric extension of
the elliptical distribution in the high dimensional regime. In detail, the
proposed method distributes the $d$-dimensional data of size $N$ generated from
a transellipti... | computer science |
21,951 | Double Coupled Canonical Polyadic Decomposition for Joint Blind Source
Separation | stat.ML | Joint blind source separation (J-BSS) is an emerging data-driven technique
for multi-set data-fusion. In this paper, J-BSS is addressed from a tensorial
perspective. We show how, by using second-order multi-set statistics in J-BSS,
a specific double coupled canonical polyadic decomposition (DC-CPD) problem can
be formu... | computer science |
21,952 | Variable importance in binary regression trees and forests | stat.ML | We characterize and study variable importance (VIMP) and pairwise variable
associations in binary regression trees. A key component involves the node mean
squared error for a quantity we refer to as a maximal subtree. The theory
naturally extends from single trees to ensembles of trees and applies to
methods like rando... | computer science |
21,953 | Security Analysis of Online Centroid Anomaly Detection | stat.ML | Security issues are crucial in a number of machine learning applications,
especially in scenarios dealing with human activity rather than natural
phenomena (e.g., information ranking, spam detection, malware detection, etc.).
It is to be expected in such cases that learning algorithms will have to deal
with manipulated... | computer science |
21,954 | Supervised Topic Models | stat.ML | We introduce supervised latent Dirichlet allocation (sLDA), a statistical
model of labelled documents. The model accommodates a variety of response
types. We derive an approximate maximum-likelihood procedure for parameter
estimation, which relies on variational methods to handle intractable posterior
expectations. Pre... | computer science |
21,955 | Optimal Allocation Strategies for the Dark Pool Problem | stat.ML | We study the problem of allocating stocks to dark pools. We propose and
analyze an optimal approach for allocations, if continuous-valued allocations
are allowed. We also propose a modification for the case when only
integer-valued allocations are possible. We extend the previous work on this
problem to adversarial sce... | computer science |
21,956 | Linear Time Feature Selection for Regularized Least-Squares | stat.ML | We propose a novel algorithm for greedy forward feature selection for
regularized least-squares (RLS) regression and classification, also known as
the least-squares support vector machine or ridge regression. The algorithm,
which we call greedy RLS, starts from the empty feature set, and on each
iteration adds the feat... | computer science |
21,957 | Euclidean Distances, soft and spectral Clustering on Weighted Graphs | stat.ML | We define a class of Euclidean distances on weighted graphs, enabling to
perform thermodynamic soft graph clustering. The class can be constructed form
the "raw coordinates" encountered in spectral clustering, and can be extended
by means of higher-dimensional embeddings (Schoenberg transformations).
Geographical flow ... | computer science |
21,958 | Clustering Stability: An Overview | stat.ML | A popular method for selecting the number of clusters is based on stability
arguments: one chooses the number of clusters such that the corresponding
clustering results are "most stable". In recent years, a series of papers has
analyzed the behavior of this method from a theoretical point of view. However,
the results ... | computer science |
21,959 | Directional Statistics on Permutations | stat.ML | Distributions over permutations arise in applications ranging from
multi-object tracking to ranking of instances. The difficulty of dealing with
these distributions is caused by the size of their domain, which is factorial
in the number of considered entities ($n!$). It makes the direct definition of
a multinomial dist... | computer science |
21,960 | Reduced Rank Vector Generalized Linear Models for Feature Extraction | stat.ML | Supervised linear feature extraction can be achieved by fitting a reduced
rank multivariate model. This paper studies rank penalized and rank constrained
vector generalized linear models. From the perspective of thresholding rules,
we build a framework for fitting singular value penalized models and use it for
feature ... | computer science |
21,961 | Support Vector Machines for Additive Models: Consistency and Robustness | stat.ML | Support vector machines (SVMs) are special kernel based methods and belong to
the most successful learning methods since more than a decade. SVMs can
informally be described as a kind of regularized M-estimators for functions and
have demonstrated their usefulness in many complicated real-life problems.
During the last... | computer science |
21,962 | A unifying view for performance measures in multi-class prediction | stat.ML | In the last few years, many different performance measures have been
introduced to overcome the weakness of the most natural metric, the Accuracy.
Among them, Matthews Correlation Coefficient has recently gained popularity
among researchers not only in machine learning but also in several application
fields such as bio... | computer science |
21,963 | A Simple CW-SSIM Kernel-based Nearest Neighbor Method for Handwritten
Digit Classification | stat.ML | We propose a simple kernel based nearest neighbor approach for handwritten
digit classification. The "distance" here is actually a kernel defining the
similarity between two images. We carefully study the effects of different
number of neighbors and weight schemes and report the results. With only a few
nearest neighbo... | computer science |
21,964 | Kernel induced random survival forests | stat.ML | Kernel Induced Random Survival Forests (KIRSF) is a statistical learning
algorithm which aims to improve prediction accuracy for survival data. As in
Random Survival Forests (RSF), Cumulative Hazard Function is predicted for each
individual in the test set. Prediction error is estimated using Harrell's
concordance inde... | computer science |
21,965 | Sparse Group Restricted Boltzmann Machines | stat.ML | Since learning is typically very slow in Boltzmann machines, there is a need
to restrict connections within hidden layers. However, the resulting states of
hidden units exhibit statistical dependencies. Based on this observation, we
propose using $l_1/l_2$ regularization upon the activation possibilities of
hidden unit... | computer science |
21,966 | Union Support Recovery in Multi-task Learning | stat.ML | We sharply characterize the performance of different penalization schemes for
the problem of selecting the relevant variables in the multi-task setting.
Previous work focuses on the regression problem where conditions on the design
matrix complicate the analysis. A clearer and simpler picture emerges by
studying the No... | computer science |
21,967 | Information-Maximization Clustering based on Squared-Loss Mutual
Information | stat.ML | Information-maximization clustering learns a probabilistic classifier in an
unsupervised manner so that mutual information between feature vectors and
cluster assignments is maximized. A notable advantage of this approach is that
it only involves continuous optimization of model parameters, which is
substantially easie... | computer science |
21,968 | Asynchronous Stochastic Approximation with Differential Inclusions | stat.ML | The asymptotic pseudo-trajectory approach to stochastic approximation of
Benaim, Hofbauer and Sorin is extended for asynchronous stochastic
approximations with a set-valued mean field. The asynchronicity of the process
is incorporated into the mean field to produce convergence results which remain
similar to those of a... | computer science |
21,969 | Convergent Expectation Propagation in Linear Models with Spike-and-slab
Priors | stat.ML | Exact inference in the linear regression model with spike and slab priors is
often intractable. Expectation propagation (EP) can be used for approximate
inference. However, the regular sequential form of EP (R-EP) may fail to
converge in this model when the size of the training set is very small. As an
alternative, we ... | computer science |
21,970 | Graph Construction for Learning with Unbalanced Data | stat.ML | Unbalanced data arises in many learning tasks such as clustering of
multi-class data, hierarchical divisive clustering and semisupervised learning.
Graph-based approaches are popular tools for these problems. Graph construction
is an important aspect of graph-based learning. We show that graph-based
algorithms can fail... | computer science |
21,971 | Ensemble Models with Trees and Rules | stat.ML | In this article, we have proposed several approaches for post processing a
large ensemble of prediction models or rules. The results from our simulations
show that the post processing methods we have considered here are promising. We
have used the techniques developed here for estimation of quantitative traits
from mar... | computer science |
21,972 | Regret lower bounds and extended Upper Confidence Bounds policies in
stochastic multi-armed bandit problem | stat.ML | This paper is devoted to regret lower bounds in the classical model of
stochastic multi-armed bandit. A well-known result of Lai and Robbins, which
has then been extended by Burnetas and Katehakis, has established the presence
of a logarithmic bound for all consistent policies. We relax the notion of
consistence, and e... | computer science |
21,973 | Kernels and Submodels of Deep Belief Networks | stat.ML | We study the mixtures of factorizing probability distributions represented as
visible marginal distributions in stochastic layered networks. We take the
perspective of kernel transitions of distributions, which gives a unified
picture of distributed representations arising from Deep Belief Networks (DBN)
and other netw... | computer science |
21,974 | Kernelized Bayesian Matrix Factorization | stat.ML | We extend kernelized matrix factorization with a fully Bayesian treatment and
with an ability to work with multiple side information sources expressed as
different kernels. Kernel functions have been introduced to matrix
factorization to integrate side information about the rows and columns (e.g.,
objects and users in ... | computer science |
21,975 | Gradient density estimation in arbitrary finite dimensions using the
method of stationary phase | stat.ML | We prove that the density function of the gradient of a sufficiently smooth
function $S : \Omega \subset \mathbb{R}^d \rightarrow \mathbb{R}$, obtained via
a random variable transformation of a uniformly distributed random variable, is
increasingly closely approximated by the normalized power spectrum of
$\phi=\exp\lef... | computer science |
21,976 | A Truncated EM Approach for Spike-and-Slab Sparse Coding | stat.ML | We study inference and learning based on a sparse coding model with
`spike-and-slab' prior. As in standard sparse coding, the model used assumes
independent latent sources that linearly combine to generate data points.
However, instead of using a standard sparse prior such as a Laplace
distribution, we study the applic... | computer science |
21,977 | Statistical inference on errorfully observed graphs | stat.ML | Statistical inference on graphs is a burgeoning field in the applied and
theoretical statistics communities, as well as throughout the wider world of
science, engineering, business, etc. In many applications, we are faced with
the reality of errorfully observed graphs. That is, the existence of an edge
between two vert... | computer science |
21,978 | Random Input Sampling for Complex Models Using Markov Chain Monte Carlo | stat.ML | Many random processes can be simulated as the output of a deterministic model
accepting random inputs. Such a model usually describes a complex mathematical
or physical stochastic system and the randomness is introduced in the input
variables of the model. When the statistics of the output event are known,
these input ... | computer science |
21,979 | Exact and Efficient Parallel Inference for Nonparametric Mixture Models | stat.ML | Nonparametric mixture models based on the Dirichlet process are an elegant
alternative to finite models when the number of underlying components is
unknown, but inference in such models can be slow. Existing attempts to
parallelize inference in such models have relied on introducing approximations,
which can lead to in... | computer science |
21,980 | Sparse Principal Component Analysis for High Dimensional Vector
Autoregressive Models | stat.ML | We study sparse principal component analysis for high dimensional vector
autoregressive time series under a doubly asymptotic framework, which allows
the dimension $d$ to scale with the series length $T$. We treat the transition
matrix of time series as a nuisance parameter and directly apply sparse
principal component... | computer science |
21,981 | A Direct Estimation of High Dimensional Stationary Vector
Autoregressions | stat.ML | The vector autoregressive (VAR) model is a powerful tool in modeling complex
time series and has been exploited in many fields. However, fitting high
dimensional VAR model poses some unique challenges: On one hand, the
dimensionality, caused by modeling a large number of time series and higher
order autoregressive proc... | computer science |
21,982 | Dimensionality Detection and Integration of Multiple Data Sources via
the GP-LVM | stat.ML | The Gaussian Process Latent Variable Model (GP-LVM) is a non-linear
probabilistic method of embedding a high dimensional dataset in terms low
dimensional `latent' variables. In this paper we illustrate that maximum a
posteriori (MAP) estimation of the latent variables and hyperparameters can be
used for model selection... | computer science |
21,983 | Gaussian Process Conditional Copulas with Applications to Financial Time
Series | stat.ML | The estimation of dependencies between multiple variables is a central
problem in the analysis of financial time series. A common approach is to
express these dependencies in terms of a copula function. Typically the copula
function is assumed to be constant but this may be inaccurate when there are
covariates that cou... | computer science |
21,984 | The blessing of transitivity in sparse and stochastic networks | stat.ML | The interaction between transitivity and sparsity, two common features in
empirical networks, implies that there are local regions of large sparse
networks that are dense. We call this the blessing of transitivity and it has
consequences for both modeling and inference. Extant research suggests that
statistical inferen... | computer science |
21,985 | Optimisation dans la détection de communautés recouvrantes et
équilibre de Nash | stat.ML | Community detection in graphs has been the subject of many algorithms. Recent
methods want to optimize a modularity function which shows a maximum of
relationships within communities and found a minimum of inter-community
relations. these algorithms are applied to unipartite, multipartite and
directed graphs. However, ... | computer science |
21,986 | Thompson Sampling for 1-Dimensional Exponential Family Bandits | stat.ML | Thompson Sampling has been demonstrated in many complex bandit models,
however the theoretical guarantees available for the parametric multi-armed
bandit are still limited to the Bernoulli case. Here we extend them by proving
asymptotic optimality of the algorithm using the Jeffreys prior for
1-dimensional exponential ... | computer science |
21,987 | Kinetic Energy Plus Penalty Functions for Sparse Estimation | stat.ML | In this paper we propose and study a family of sparsity-inducing penalty
functions. Since the penalty functions are related to the kinetic energy in
special relativity, we call them \emph{kinetic energy plus} (KEP) functions. We
construct the KEP function by using the concave conjugate of a
$\chi^2$-distance function a... | computer science |
21,988 | Borel Isomorphic Dimensionality Reduction of Data and Supervised
Learning | stat.ML | In this project we further investigate the idea of reducing the
dimensionality of datasets using a Borel isomorphism with the purpose of
subsequently applying supervised learning algorithms, as originally suggested
by my supervisor V. Pestov (in 2011 Dagstuhl preprint). Any consistent learning
algorithm, for example kN... | computer science |
21,989 | Jointly Clustering Rows and Columns of Binary Matrices: Algorithms and
Trade-offs | stat.ML | In standard clustering problems, data points are represented by vectors, and
by stacking them together, one forms a data matrix with row or column cluster
structure. In this paper, we consider a class of binary matrices, arising in
many applications, which exhibit both row and column cluster structure, and our
goal is ... | computer science |
21,990 | Perfect Clustering for Stochastic Blockmodel Graphs via Adjacency
Spectral Embedding | stat.ML | Vertex clustering in a stochastic blockmodel graph has wide applicability and
has been the subject of extensive research. In thispaper, we provide a short
proof that the adjacency spectral embedding can be used to obtain perfect
clustering for the stochastic blockmodel and the degree-corrected stochastic
blockmodel. We... | computer science |
21,991 | Moments and Root-Mean-Square Error of the Bayesian MMSE Estimator of
Classification Error in the Gaussian Model | stat.ML | The most important aspect of any classifier is its error rate, because this
quantifies its predictive capacity. Thus, the accuracy of error estimation is
critical. Error estimation is problematic in small-sample classifier design
because the error must be estimated using the same data from which the
classifier has been... | computer science |
21,992 | Dependence Measure for non-additive model | stat.ML | We proposed a new statistical dependency measure called Copula Dependency
Coefficient(CDC) for two sets of variables based on copula. It is robust to
outliers, easy to implement, powerful and appropriate to high-dimensional
variables. These properties are important in many applications. Experimental
results show that C... | computer science |
21,993 | Mean Field Bayes Backpropagation: scalable training of multilayer neural
networks with binary weights | stat.ML | Significant success has been reported recently using deep neural networks for
classification. Such large networks can be computationally intensive, even
after training is over. Implementing these trained networks in hardware chips
with a limited precision of synaptic weights may improve their speed and energy
efficienc... | computer science |
21,994 | Distance-weighted Support Vector Machine | stat.ML | A novel linear classification method that possesses the merits of both the
Support Vector Machine (SVM) and the Distance-weighted Discrimination (DWD) is
proposed in this article. The proposed Distance-weighted Support Vector Machine
method can be viewed as a hybrid of SVM and DWD that finds the classification
directio... | computer science |
21,995 | Flexible High-dimensional Classification Machines and Their Asymptotic
Properties | stat.ML | Classification is an important topic in statistics and machine learning with
great potential in many real applications. In this paper, we investigate two
popular large margin classification methods, Support Vector Machine (SVM) and
Distance Weighted Discrimination (DWD), under two contexts: the
high-dimensional, low-sa... | computer science |
21,996 | ECA: High Dimensional Elliptical Component Analysis in non-Gaussian
Distributions | stat.ML | We present a robust alternative to principal component analysis (PCA) ---
called elliptical component analysis (ECA) --- for analyzing high dimensional,
elliptically distributed data. ECA estimates the eigenspace of the covariance
matrix of the elliptical data. To cope with heavy-tailed elliptical
distributions, a mult... | computer science |
21,997 | Alternating Minimization for Mixed Linear Regression | stat.ML | Mixed linear regression involves the recovery of two (or more) unknown
vectors from unlabeled linear measurements; that is, where each sample comes
from exactly one of the vectors, but we do not know which one. It is a classic
problem, and the natural and empirically most popular approach to its solution
has been the E... | computer science |
21,998 | Fast Computation of Wasserstein Barycenters | stat.ML | We present new algorithms to compute the mean of a set of empirical
probability measures under the optimal transport metric. This mean, known as
the Wasserstein barycenter, is the measure that minimizes the sum of its
Wasserstein distances to each element in that set. We propose two original
algorithms to compute Wasse... | computer science |
21,999 | Disease Prediction based on Functional Connectomes using a Scalable and
Spatially-Informed Support Vector Machine | stat.ML | Substantial evidence indicates that major psychiatric disorders are
associated with distributed neural dysconnectivity, leading to strong interest
in using neuroimaging methods to accurately predict disorder status. In this
work, we are specifically interested in a multivariate approach that uses
features derived from ... | computer science |
22,000 | Variational Bayesian inference for linear and logistic regression | stat.ML | The article describe the model, derivation, and implementation of variational
Bayesian inference for linear and logistic regression, both with and without
automatic relevance determination. It has the dual function of acting as a
tutorial for the derivation of variational Bayesian inference for simple
models, as well a... | computer science |
22,001 | Universalities of Reproducing Kernels Revisited | stat.ML | Kernel methods have been widely applied to machine learning and other
questions of approximating an unknown function from its finite sample data. To
ensure arbitrary accuracy of such approximation, various denseness conditions
are imposed on the selected kernel. This note contributes to the study of
universal, characte... | computer science |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.