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22,402 | Extreme Stochastic Variational Inference: Distributed and Asynchronous | stat.ML | We propose extreme stochastic variational inference (ESVI), an asynchronous
and lock-free algorithm to perform variational inference on massive real world
datasets. Stochastic variational inference (SVI), the state-of-the-art
algorithm for scaling variational inference to large-datasets, is inherently
serial. Moreover,... | computer science |
22,403 | Continuation of Nesterov's Smoothing for Regression with Structured
Sparsity in High-Dimensional Neuroimaging | stat.ML | Predictive models can be used on high-dimensional brain images for diagnosis
of a clinical condition. Spatial regularization through structured sparsity
offers new perspectives in this context and reduces the risk of overfitting the
model while providing interpretable neuroimaging signatures by forcing the
solution to ... | computer science |
22,404 | Identifying Outliers using Influence Function of Multiple Kernel
Canonical Correlation Analysis | stat.ML | Imaging genetic research has essentially focused on discovering unique and
co-association effects, but typically ignoring to identify outliers or atypical
objects in genetic as well as non-genetics variables. Identifying significant
outliers is an essential and challenging issue for imaging genetics and
multiple source... | computer science |
22,405 | Gene-Gene association for Imaging Genetics Data using Robust Kernel
Canonical Correlation Analysis | stat.ML | In genome-wide interaction studies, to detect gene-gene interactions, most
methods are divided into two folds: single nucleotide polymorphisms (SNP) based
and gene-based methods. Basically, the methods based on the gene are more
effective than the methods based on a single SNP. Recent years, while the
kernel canonical ... | computer science |
22,406 | Generalized Root Models: Beyond Pairwise Graphical Models for Univariate
Exponential Families | stat.ML | We present a novel k-way high-dimensional graphical model called the
Generalized Root Model (GRM) that explicitly models dependencies between
variable sets of size k > 2---where k = 2 is the standard pairwise graphical
model. This model is based on taking the k-th root of the original sufficient
statistics of any univa... | computer science |
22,407 | High Dimensional Multivariate Regression and Precision Matrix Estimation
via Nonconvex Optimization | stat.ML | We propose a nonconvex estimator for joint multivariate regression and
precision matrix estimation in the high dimensional regime, under sparsity
constraints. A gradient descent algorithm with hard thresholding is developed
to solve the nonconvex estimator, and it attains a linear rate of convergence
to the true regres... | computer science |
22,408 | Nonlinear Statistical Learning with Truncated Gaussian Graphical Models | stat.ML | We introduce the truncated Gaussian graphical model (TGGM) as a novel
framework for designing statistical models for nonlinear learning. A TGGM is a
Gaussian graphical model (GGM) with a subset of variables truncated to be
nonnegative. The truncated variables are assumed latent and integrated out to
induce a marginal m... | computer science |
22,409 | ROCS-Derived Features for Virtual Screening | stat.ML | Rapid overlay of chemical structures (ROCS) is a standard tool for the
calculation of 3D shape and chemical ("color") similarity. ROCS uses unweighted
sums to combine many aspects of similarity, yielding parameter-free models for
virtual screening. In this report, we decompose the ROCS color force field into
"color com... | computer science |
22,410 | On Robustness of Kernel Clustering | stat.ML | Clustering is one of the most important unsupervised problems in machine
learning and statistics. Among many existing algorithms, kernel k-means has
drawn much research attention due to its ability to find non-linear cluster
boundaries and its inherent simplicity. There are two main approaches for
kernel k-means: SVD o... | computer science |
22,411 | Reducing the error of Monte Carlo Algorithms by Learning Control
Variates | stat.ML | Monte Carlo (MC) sampling algorithms are an extremely widely-used technique
to estimate expectations of functions f(x), especially in high dimensions.
Control variates are a very powerful technique to reduce the error of such
estimates, but in their conventional form rely on having an accurate
approximation of f, a pri... | computer science |
22,412 | Better Conditional Density Estimation for Neural Networks | stat.ML | The vast majority of the neural network literature focuses on predicting
point values for a given set of response variables, conditioned on a feature
vector. In many cases we need to model the full joint conditional distribution
over the response variables rather than simply making point predictions. In
this paper, we ... | computer science |
22,413 | A Locally Adaptive Normal Distribution | stat.ML | The multivariate normal density is a monotonic function of the distance to
the mean, and its ellipsoidal shape is due to the underlying Euclidean metric.
We suggest to replace this metric with a locally adaptive, smoothly changing
(Riemannian) metric that favors regions of high local density. The resulting
locally adap... | computer science |
22,414 | Prediction performance after learning in Gaussian process regression | stat.ML | This paper considers the quantification of the prediction performance in
Gaussian process regression. The standard approach is to base the prediction
error bars on the theoretical predictive variance, which is a lower bound on
the mean square-error (MSE). This approach, however, does not take into account
that the stat... | computer science |
22,415 | Recursive nonlinear-system identification using latent variables | stat.ML | In this paper we develop a method for learning nonlinear systems with
multiple outputs and inputs. We begin by modelling the errors of a nominal
predictor of the system using a latent variable framework. Then using the
maximum likelihood principle we derive a criterion for learning the model. The
resulting optimization... | computer science |
22,416 | Network Maximal Correlation | stat.ML | We introduce Network Maximal Correlation (NMC) as a multivariate measure of
nonlinear association among random variables. NMC is defined via an
optimization that infers transformations of variables by maximizing aggregate
inner products between transformed variables. For finite discrete and jointly
Gaussian random vari... | computer science |
22,417 | Understanding Probabilistic Sparse Gaussian Process Approximations | stat.ML | Good sparse approximations are essential for practical inference in Gaussian
Processes as the computational cost of exact methods is prohibitive for large
datasets. The Fully Independent Training Conditional (FITC) and the Variational
Free Energy (VFE) approximations are two recent popular methods. Despite
superficial ... | computer science |
22,418 | Assessing and tuning brain decoders: cross-validation, caveats, and
guidelines | stat.ML | Decoding, ie prediction from brain images or signals, calls for empirical
evaluation of its predictive power. Such evaluation is achieved via
cross-validation, a method also used to tune decoders' hyper-parameters. This
paper is a review on cross-validation procedures for decoding in neuroimaging.
It includes a didacti... | computer science |
22,419 | The Mondrian Kernel | stat.ML | We introduce the Mondrian kernel, a fast random feature approximation to the
Laplace kernel. It is suitable for both batch and online learning, and admits a
fast kernel-width-selection procedure as the random features can be re-used
efficiently for all kernel widths. The features are constructed by sampling
trees via a... | computer science |
22,420 | The Effect of Heteroscedasticity on Regression Trees | stat.ML | Regression trees are becoming increasingly popular as omnibus predicting
tools and as the basis of numerous modern statistical learning ensembles. Part
of their popularity is their ability to create a regression prediction without
ever specifying a structure for the mean model. However, the method implicitly
assumes ho... | computer science |
22,421 | Making Tree Ensembles Interpretable | stat.ML | Tree ensembles, such as random forest and boosted trees, are renowned for
their high prediction performance, whereas their interpretability is critically
limited. In this paper, we propose a post processing method that improves the
model interpretability of tree ensembles. After learning a complex tree
ensembles in a s... | computer science |
22,422 | PSF : Introduction to R Package for Pattern Sequence Based Forecasting
Algorithm | stat.ML | This paper discusses about an R package that implements the Pattern Sequence
based Forecasting (PSF) algorithm, which was developed for univariate time
series forecasting. This algorithm has been successfully applied to many
different fields. The PSF algorithm consists of two major parts: clustering and
prediction. The... | computer science |
22,423 | Interpretability in Linear Brain Decoding | stat.ML | Improving the interpretability of brain decoding approaches is of primary
interest in many neuroimaging studies. Despite extensive studies of this type,
at present, there is no formal definition for interpretability of brain
decoding models. As a consequence, there is no quantitative measure for
evaluating the interpre... | computer science |
22,424 | Tight Performance Bounds for Compressed Sensing With Group Sparsity | stat.ML | Compressed sensing refers to the recovery of a high-dimensional but sparse
vector using a small number of linear measurements. Minimizing the
$\ell_1$-norm is among the more popular approaches for compressed sensing. A
recent paper by Cai and Zhang has provided the "best possible" bounds for
$\ell_1$-norm minimization ... | computer science |
22,425 | Dealing with a large number of classes -- Likelihood, Discrimination or
Ranking? | stat.ML | We consider training probabilistic classifiers in the case of a large number
of classes. The number of classes is assumed too large to perform exact
normalisation over all classes. To account for this we consider a simple
approach that directly approximates the likelihood. We show that this simple
approach works well o... | computer science |
22,426 | PAC-Bayesian Analysis for a two-step Hierarchical Multiview Learning
Approach | stat.ML | We study a two-level multiview learning with more than two views under the
PAC-Bayesian framework. This approach, sometimes referred as late fusion,
consists in learning sequentially multiple view-specific classifiers at the
first level, and then combining these view-specific classifiers at the second
level. Our main t... | computer science |
22,427 | Modeling Group Dynamics Using Probabilistic Tensor Decompositions | stat.ML | We propose a probabilistic modeling framework for learning the dynamic
patterns in the collective behaviors of social agents and developing profiles
for different behavioral groups, using data collected from multiple information
sources. The proposed model is based on a hierarchical Bayesian process, in
which each obse... | computer science |
22,428 | The Dependent Random Measures with Independent Increments in Mixture
Models | stat.ML | When observations are organized into groups where commonalties exist amongst
them, the dependent random measures can be an ideal choice for modeling. One of
the propositions of the dependent random measures is that the atoms of the
posterior distribution are shared amongst groups, and hence groups can borrow
informatio... | computer science |
22,429 | Anomaly detection in video with Bayesian nonparametrics | stat.ML | A novel dynamic Bayesian nonparametric topic model for anomaly detection in
video is proposed in this paper. Batch and online Gibbs samplers are developed
for inference. The paper introduces a new abnormality measure for decision
making. The proposed method is evaluated on both synthetic and real data. The
comparison w... | computer science |
22,430 | Dynamic Hierarchical Dirichlet Process for Abnormal Behaviour Detection
in Video | stat.ML | This paper proposes a novel dynamic Hierarchical Dirichlet Process topic
model that considers the dependence between successive observations.
Conventional posterior inference algorithms for this kind of models require
processing of the whole data through several passes. It is computationally
intractable for massive or ... | computer science |
22,431 | Automatic Variational ABC | stat.ML | Approximate Bayesian Computation (ABC) is a framework for performing
likelihood-free posterior inference for simulation models. Stochastic
Variational inference (SVI) is an appealing alternative to the inefficient
sampling approaches commonly used in ABC. However, SVI is highly sensitive to
the variance of the gradient... | computer science |
22,432 | Modeling Industrial ADMET Data with Multitask Networks | stat.ML | Deep learning methods such as multitask neural networks have recently been
applied to ligand-based virtual screening and other drug discovery
applications. Using a set of industrial ADMET datasets, we compare neural
networks to standard baseline models and analyze multitask learning effects
with both random cross-valid... | computer science |
22,433 | Tracking Switched Dynamic Network Topologies from Information Cascades | stat.ML | Contagions such as the spread of popular news stories, or infectious
diseases, propagate in cascades over dynamic networks with unobservable
topologies. However, "social signals" such as product purchase time, or blog
entry timestamps are measurable, and implicitly depend on the underlying
topology, making it possible ... | computer science |
22,434 | Alternating Estimation for Structured High-Dimensional Multi-Response
Models | stat.ML | We consider learning high-dimensional multi-response linear models with
structured parameters. By exploiting the noise correlations among responses, we
propose an alternating estimation (AltEst) procedure to estimate the model
parameters based on the generalized Dantzig selector. Under suitable sample
size and resampli... | computer science |
22,435 | Making Tree Ensembles Interpretable: A Bayesian Model Selection Approach | stat.ML | Tree ensembles, such as random forests and boosted trees, are renowned for
their high prediction performance. However, their interpretability is
critically limited due to the enormous complexity. In this study, we present a
method to make a complex tree ensemble interpretable by simplifying the model.
Specifically, we ... | computer science |
22,436 | On Mixed Memberships and Symmetric Nonnegative Matrix Factorizations | stat.ML | The problem of finding overlapping communities in networks has gained much
attention recently. Optimization-based approaches use non-negative matrix
factorization (NMF) or variants, but the global optimum cannot be provably
attained in general. Model-based approaches, such as the popular
mixed-membership stochastic blo... | computer science |
22,437 | A Semi-supervised learning approach to enhance health care
Community-based Question Answering: A case study in alcoholism | stat.ML | Community-based Question Answering (CQA) sites play an important role in
addressing health information needs. However, a significant number of posted
questions remain unanswered. Automatically answering the posted questions can
provide a useful source of information for online health communities. In this
study, we deve... | computer science |
22,438 | Automatic Generation of Probabilistic Programming from Time Series Data | stat.ML | Probabilistic programming languages represent complex data with intermingled
models in a few lines of code. Efficient inference algorithms in probabilistic
programming languages make possible to build unified frameworks to compute
interesting probabilities of various large, real-world problems. When the
structure of mo... | computer science |
22,439 | On the Consistency of the Likelihood Maximization Vertex Nomination
Scheme: Bridging the Gap Between Maximum Likelihood Estimation and Graph
Matching | stat.ML | Given a graph in which a few vertices are deemed interesting a priori, the
vertex nomination task is to order the remaining vertices into a nomination
list such that there is a concentration of interesting vertices at the top of
the list. Previous work has yielded several approaches to this problem, with
theoretical re... | computer science |
22,440 | Bayesian nonparametrics for Sparse Dynamic Networks | stat.ML | We propose a Bayesian nonparametric prior for time-varying networks. To each
node of the network is associated a positive parameter, modeling the
sociability of that node. Sociabilities are assumed to evolve over time, and
are modeled via a dynamic point process model. The model is able to (a) capture
smooth evolution ... | computer science |
22,441 | Kernel Bayesian Inference with Posterior Regularization | stat.ML | We propose a vector-valued regression problem whose solution is equivalent to
the reproducing kernel Hilbert space (RKHS) embedding of the Bayesian posterior
distribution. This equivalence provides a new understanding of kernel Bayesian
inference. Moreover, the optimization problem induces a new regularization for
the ... | computer science |
22,442 | A Classification Framework for Partially Observed Dynamical Systems | stat.ML | We present a general framework for classifying partially observed dynamical
systems based on the idea of learning in the model space. In contrast to the
existing approaches using model point estimates to represent individual data
items, we employ posterior distributions over models, thus taking into account
in a princi... | computer science |
22,443 | Sparse additive Gaussian process with soft interactions | stat.ML | Additive nonparametric regression models provide an attractive tool for
variable selection in high dimensions when the relationship between the
response and predictors is complex. They offer greater flexibility compared to
parametric non-linear regression models and better interpretability and
scalability than the non-... | computer science |
22,444 | Bayesian quantile additive regression trees | stat.ML | Ensemble of regression trees have become popular statistical tools for the
estimation of conditional mean given a set of predictors. However, quantile
regression trees and their ensembles have not yet garnered much attention
despite the increasing popularity of the linear quantile regression model. This
work proposes a... | computer science |
22,445 | Magnetic Hamiltonian Monte Carlo | stat.ML | Hamiltonian Monte Carlo (HMC) exploits Hamiltonian dynamics to construct
efficient proposals for Markov chain Monte Carlo (MCMC). In this paper, we
present a generalization of HMC which exploits \textit{non-canonical}
Hamiltonian dynamics. We refer to this algorithm as magnetic HMC, since in 3
dimensions a subset of th... | computer science |
22,446 | Retrospective Causal Inference with Machine Learning Ensembles: An
Application to Anti-Recidivism Policies in Colombia | stat.ML | We present new methods to estimate causal effects retrospectively from micro
data with the assistance of a machine learning ensemble. This approach
overcomes two important limitations in conventional methods like regression
modeling or matching: (i) ambiguity about the pertinent retrospective
counterfactuals and (ii) p... | computer science |
22,447 | From Dependence to Causation | stat.ML | Machine learning is the science of discovering statistical dependencies in
data, and the use of those dependencies to perform predictions. During the last
decade, machine learning has made spectacular progress, surpassing human
performance in complex tasks such as object recognition, car driving, and
computer gaming. H... | computer science |
22,448 | Effects of Additional Data on Bayesian Clustering | stat.ML | Hierarchical probabilistic models, such as mixture models, are used for
cluster analysis. These models have two types of variables: observable and
latent. In cluster analysis, the latent variable is estimated, and it is
expected that additional information will improve the accuracy of the
estimation of the latent varia... | computer science |
22,449 | Kernel Density Estimation for Dynamical Systems | stat.ML | We study the density estimation problem with observations generated by
certain dynamical systems that admit a unique underlying invariant Lebesgue
density. Observations drawn from dynamical systems are not independent and
moreover, usual mixing concepts may not be appropriate for measuring the
dependence among these ob... | computer science |
22,450 | Safe Policy Improvement by Minimizing Robust Baseline Regret | stat.ML | An important problem in sequential decision-making under uncertainty is to
use limited data to compute a safe policy, i.e., a policy that is guaranteed to
perform at least as well as a given baseline strategy. In this paper, we
develop and analyze a new model-based approach to compute a safe policy when we
have access ... | computer science |
22,451 | Spectral Echolocation via the Wave Embedding | stat.ML | Spectral embedding uses eigenfunctions of the discrete Laplacian on a
weighted graph to obtain coordinates for an embedding of an abstract data set
into Euclidean space. We propose a new pre-processing step of first using the
eigenfunctions to simulate a low-frequency wave moving over the data and using
both position a... | computer science |
22,452 | Nested Kriging predictions for datasets with large number of
observations | stat.ML | This work falls within the context of predicting the value of a real function
at some input locations given a limited number of observations of this
function. The Kriging interpolation technique (or Gaussian process regression)
is often considered to tackle such a problem but the method suffers from its
computational b... | computer science |
22,453 | Distribution-dependent concentration inequalities for tighter
generalization bounds | stat.ML | Concentration inequalities are indispensable tools for studying the
generalization capacity of learning models. Hoeffding's and McDiarmid's
inequalities are commonly used, giving bounds independent of the data
distribution. Although this makes them widely applicable, a drawback is that
the bounds can be too loose in so... | computer science |
22,454 | Anomaly Detection and Localisation using Mixed Graphical Models | stat.ML | We propose a method that performs anomaly detection and localisation within
heterogeneous data using a pairwise undirected mixed graphical model. The data
are a mixture of categorical and quantitative variables, and the model is
learned over a dataset that is supposed not to contain any anomaly. We then use
the model o... | computer science |
22,455 | The Landscape of Empirical Risk for Non-convex Losses | stat.ML | Most high-dimensional estimation and prediction methods propose to minimize a
cost function (empirical risk) that is written as a sum of losses associated to
each data point. In this paper we focus on the case of non-convex losses, which
is practically important but still poorly understood. Classical empirical
process ... | computer science |
22,456 | Variational Mixture Models with Gamma or inverse-Gamma components | stat.ML | Mixture models with Gamma and or inverse-Gamma distributed mixture components
are useful for medical image tissue segmentation or as post-hoc models for
regression coefficients obtained from linear regression within a Generalised
Linear Modeling framework (GLM), used in this case to separate stochastic
(Gaussian) noise... | computer science |
22,457 | Preterm Birth Prediction: Deriving Stable and Interpretable Rules from
High Dimensional Data | stat.ML | Preterm births occur at an alarming rate of 10-15%. Preemies have a higher
risk of infant mortality, developmental retardation and long-term disabilities.
Predicting preterm birth is difficult, even for the most experienced
clinicians. The most well-designed clinical study thus far reaches a modest
sensitivity of 18.2-... | computer science |
22,458 | Limit theorems for eigenvectors of the normalized Laplacian for random
graphs | stat.ML | We prove a central limit theorem for the components of the eigenvectors
corresponding to the $d$ largest eigenvalues of the normalized Laplacian matrix
of a finite dimensional random dot product graph. As a corollary, we show that
for stochastic blockmodel graphs, the rows of the spectral embedding of the
normalized La... | computer science |
22,459 | Kernel Risk-Sensitive Loss: Definition, Properties and Application to
Robust Adaptive Filtering | stat.ML | Nonlinear similarity measures defined in kernel space, such as correntropy,
can extract higher-order statistics of data and offer potentially significant
performance improvement over their linear counterparts especially in
non-Gaussian signal processing and machine learning. In this work, we propose a
new similarity me... | computer science |
22,460 | Fast Mixing Markov Chains for Strongly Rayleigh Measures, DPPs, and
Constrained Sampling | stat.ML | We study probability measures induced by set functions with constraints. Such
measures arise in a variety of real-world settings, where prior knowledge,
resource limitations, or other pragmatic considerations impose constraints. We
consider the task of rapidly sampling from such constrained measures, and
develop fast M... | computer science |
22,461 | Fast Algorithms for Demixing Sparse Signals from Nonlinear Observations | stat.ML | We study the problem of demixing a pair of sparse signals from noisy,
nonlinear observations of their superposition. Mathematically, we consider a
nonlinear signal observation model, $y_i = g(a_i^Tx) + e_i, \ i=1,\ldots,m$,
where $x = \Phi w+\Psi z$ denotes the superposition signal, $\Phi$ and $\Psi$
are orthonormal ba... | computer science |
22,462 | Iterative Hard Thresholding for Model Selection in Genome-Wide
Association Studies | stat.ML | A genome-wide association study (GWAS) correlates marker variation with trait
variation in a sample of individuals. Each study subject is genotyped at a
multitude of SNPs (single nucleotide polymorphisms) spanning the genome. Here
we assume that subjects are unrelated and collected at random and that trait
values are n... | computer science |
22,463 | Statistical Guarantees for Estimating the Centers of a Two-component
Gaussian Mixture by EM | stat.ML | Recently, a general method for analyzing the statistical accuracy of the EM
algorithm has been developed and applied to some simple latent variable models
[Balakrishnan et al. 2016]. In that method, the basin of attraction for valid
initialization is required to be a ball around the truth. Using Stein's Lemma,
we exten... | computer science |
22,464 | Boosting as a kernel-based method | stat.ML | Boosting combines weak (biased) learners to obtain effective learning
algorithms for classification and prediction. In this paper, we show a
connection between boosting and kernel-based methods, highlighting both
theoretical and practical applications. In the context of $\ell_2$ boosting, we
start with a weak linear le... | computer science |
22,465 | Scaling Factorial Hidden Markov Models: Stochastic Variational Inference
without Messages | stat.ML | Factorial Hidden Markov Models (FHMMs) are powerful models for sequential
data but they do not scale well with long sequences. We propose a scalable
inference and learning algorithm for FHMMs that draws on ideas from the
stochastic variational inference, neural network and copula literatures. Unlike
existing approaches... | computer science |
22,466 | Ultra High-Dimensional Nonlinear Feature Selection for Big Biological
Data | stat.ML | Machine learning methods are used to discover complex nonlinear relationships
in biological and medical data. However, sophisticated learning models are
computationally unfeasible for data with millions of features. Here we
introduce the first feature selection method for nonlinear learning problems
that can scale up t... | computer science |
22,467 | Robust Volume Minimization-Based Matrix Factorization for Remote Sensing
and Document Clustering | stat.ML | This paper considers \emph{volume minimization} (VolMin)-based structured
matrix factorization (SMF). VolMin is a factorization criterion that decomposes
a given data matrix into a basis matrix times a structured coefficient matrix
via finding the minimum-volume simplex that encloses all the columns of the
data matrix.... | computer science |
22,468 | Clustering Mixed Datasets Using Homogeneity Analysis with Applications
to Big Data | stat.ML | Datasets with a mixture of numerical and categorical attributes are routinely
encountered in many application domains. In this work we examine an approach to
clustering such datasets using homogeneity analysis. Homogeneity analysis
determines a euclidean representation of the data. This can be analyzed by
leveraging th... | computer science |
22,469 | Spatial Modeling of Oil Exploration Areas Using Neural Networks and
ANFIS in GIS | stat.ML | Exploration of hydrocarbon resources is a highly complicated and expensive
process where various geological, geochemical and geophysical factors are
developed then combined together. It is highly significant how to design the
seismic data acquisition survey and locate the exploratory wells since
incorrect or imprecise ... | computer science |
22,470 | The discriminative Kalman filter for nonlinear and non-Gaussian
sequential Bayesian filtering | stat.ML | The Kalman filter (KF) is used in a variety of applications for computing the
posterior distribution of latent states in a state space model. The model
requires a linear relationship between states and observations. Extensions to
the Kalman filter have been proposed that incorporate linear approximations to
nonlinear m... | computer science |
22,471 | Formal Concept Analysis of Rodent Carriers of Zoonotic Disease | stat.ML | The technique of Formal Concept Analysis is applied to a dataset describing
the traits of rodents, with the goal of identifying zoonotic disease
carriers,or those species carrying infections that can spillover to cause human
disease. The concepts identified among these species together provide
rules-of-thumb about the ... | computer science |
22,472 | Estimating the Number of Clusters via Normalized Cluster Instability | stat.ML | We improve existing instability-based methods for the selection of the number
of clusters $k$ in cluster analysis by normalizing instability. In contrast to
existing instability methods which only perform well for bounded sequences of
small $k$, our method performs well across the whole sequence of possible $k$.
In add... | computer science |
22,473 | Maximum Correntropy Unscented Filter | stat.ML | The unscented transformation (UT) is an efficient method to solve the state
estimation problem for a non-linear dynamic system, utilizing a derivative-free
higher-order approximation by approximating a Gaussian distribution rather than
approximating a non-linear function. Applying the UT to a Kalman filter type
estimat... | computer science |
22,474 | A Randomized Approach to Efficient Kernel Clustering | stat.ML | Kernel-based K-means clustering has gained popularity due to its simplicity
and the power of its implicit non-linear representation of the data. A dominant
concern is the memory requirement since memory scales as the square of the
number of data points. We provide a new analysis of a class of approximate
kernel methods... | computer science |
22,475 | On the Computational Complexity of Geometric Langevin Monte Carlo | stat.ML | Manifold Markov chain Monte Carlo algorithms have been introduced to sample
more effectively from challenging target densities exhibiting multiple modes or
strong correlations. Such algorithms exploit the local geometry of the
parameter space, thus enabling chains to achieve a faster convergence rate when
measured in n... | computer science |
22,476 | Incremental Nonlinear System Identification and Adaptive Particle
Filtering Using Gaussian Process | stat.ML | An incremental/online state dynamic learning method is proposed for
identification of the nonlinear Gaussian state space models. The method embeds
the stochastic variational sparse Gaussian process as the probabilistic state
dynamic model inside a particle filter framework. Model updating is done at
measurement sample ... | computer science |
22,477 | Joint Estimation of Multiple Dependent Gaussian Graphical Models with
Applications to Mouse Genomics | stat.ML | Gaussian graphical models are widely used to represent conditional dependence
among random variables. In this paper, we propose a novel estimator for data
arising from a group of Gaussian graphical models that are themselves
dependent. A motivating example is that of modeling gene expression collected
on multiple tissu... | computer science |
22,478 | Understanding Trainable Sparse Coding via Matrix Factorization | stat.ML | Sparse coding is a core building block in many data analysis and machine
learning pipelines. Typically it is solved by relying on generic optimization
techniques, that are optimal in the class of first-order methods for
non-smooth, convex functions, such as the Iterative Soft Thresholding Algorithm
and its accelerated ... | computer science |
22,479 | Generic Inference in Latent Gaussian Process Models | stat.ML | We develop an automated variational method for inference in models with
Gaussian process (GP) priors and general likelihoods. The method supports
multiple outputs and multiple latent functions and does not require detailed
knowledge of the conditional likelihood, only needing its evaluation as a
black-box function. Usi... | computer science |
22,480 | Predictive Entropy Search for Multi-objective Bayesian Optimization with
Constraints | stat.ML | This work presents PESMOC, Predictive Entropy Search for Multi-objective
Bayesian Optimization with Constraints, an information-based strategy for the
simultaneous optimization of multiple expensive-to-evaluate black-box functions
under the presence of several constraints. PESMOC can hence be used to solve a
wide range... | computer science |
22,481 | Structured Sparse Principal Components Analysis with the TV-Elastic Net
penalty | stat.ML | Principal component analysis (PCA) is an exploratory tool widely used in data
analysis to uncover dominant patterns of variability within a population.
Despite its ability to represent a data set in a low-dimensional space, the
interpretability of PCA remains limited. However, in neuroimaging, it is
essential to uncove... | computer science |
22,482 | Gray-box inference for structured Gaussian process models | stat.ML | We develop an automated variational inference method for Bayesian structured
prediction problems with Gaussian process (GP) priors and linear-chain
likelihoods. Our approach does not need to know the details of the structured
likelihood model and can scale up to a large number of observations.
Furthermore, we show that... | computer science |
22,483 | Relativistic Monte Carlo | stat.ML | Hamiltonian Monte Carlo (HMC) is a popular Markov chain Monte Carlo (MCMC)
algorithm that generates proposals for a Metropolis-Hastings algorithm by
simulating the dynamics of a Hamiltonian system. However, HMC is sensitive to
large time discretizations and performs poorly if there is a mismatch between
the spatial geo... | computer science |
22,484 | Unbiased Sparse Subspace Clustering By Selective Pursuit | stat.ML | Sparse subspace clustering (SSC) is an elegant approach for unsupervised
segmentation if the data points of each cluster are located in linear
subspaces. This model applies, for instance, in motion segmentation if some
restrictions on the camera model hold. SSC requires that problems based on the
$l_1$-norm are solved ... | computer science |
22,485 | Discovering Relationships and their Structures Across Disparate Data
Modalities | stat.ML | Determining how certain properties are related to other properties is
fundamental to scientific discovery. As data collection rates accelerate, it is
becoming increasingly difficult yet ever more important to determine whether
one property of data (e.g., cloud density) is related to another (e.g., grass
wetness). Only ... | computer science |
22,486 | Saturating Splines and Feature Selection | stat.ML | We extend the adaptive regression spline model by incorporating saturation,
the natural requirement that a function extend as a constant outside a certain
range. We fit saturating splines to data using a convex optimization problem
over a space of measures, which we solve using an efficient algorithm based on
the condi... | computer science |
22,487 | Estimating Probability Distributions using "Dirac" Kernels (via
Rademacher-Walsh Polynomial Basis Functions) | stat.ML | In many applications (in particular information systems, such as pattern
recognition, machine learning, cheminformatics, bioinformatics to name but a
few) the assessment of uncertainty is essential - i.e., the estimation of the
underlying probability distribution function. More often than not, the form of
this function... | computer science |
22,488 | One-vs-Each Approximation to Softmax for Scalable Estimation of
Probabilities | stat.ML | The softmax representation of probabilities for categorical variables plays a
prominent role in modern machine learning with numerous applications in areas
such as large scale classification, neural language modeling and recommendation
systems. However, softmax estimation is very expensive for large scale
inference bec... | computer science |
22,489 | Variational Inference with Hamiltonian Monte Carlo | stat.ML | Variational inference lies at the core of many state-of-the-art algorithms.
To improve the approximation of the posterior beyond parametric families, it
was proposed to include MCMC steps into the variational lower bound. In this
work we explore this idea using steps of the Hamiltonian Monte Carlo (HMC)
algorithm, an e... | computer science |
22,490 | Online Categorical Subspace Learning for Sketching Big Data with Misses | stat.ML | With the scale of data growing every day, reducing the dimensionality (a.k.a.
sketching) of high-dimensional data has emerged as a task of paramount
importance. Relevant issues to address in this context include the sheer volume
of data that may consist of categorical samples, the typically streaming format
of acquisit... | computer science |
22,491 | Stabilizing Linear Prediction Models using Autoencoder | stat.ML | To date, the instability of prognostic predictors in a sparse high
dimensional model, which hinders their clinical adoption, has received little
attention. Stable prediction is often overlooked in favour of performance. Yet,
stability prevails as key when adopting models in critical areas as healthcare.
Our study propo... | computer science |
22,492 | A Birth and Death Process for Bayesian Network Structure Inference | stat.ML | Bayesian networks (BNs) are graphical models that are useful for representing
high-dimensional probability distributions. There has been a great deal of
interest in recent years in the NP-hard problem of learning the structure of a
BN from observed data. Typically, one assigns a score to various structures and
the sear... | computer science |
22,493 | Model Selection for Gaussian Process Regression by Approximation Set
Coding | stat.ML | Gaussian processes are powerful, yet analytically tractable models for
supervised learning. A Gaussian process is characterized by a mean function and
a covariance function (kernel), which are determined by a model selection
criterion. The functions to be compared do not just differ in their
parametrization but in thei... | computer science |
22,494 | Recovering Multiple Nonnegative Time Series From a Few Temporal
Aggregates | stat.ML | Motivated by electricity consumption metering, we extend existing nonnegative
matrix factorization (NMF) algorithms to use linear measurements as
observations, instead of matrix entries. The objective is to estimate multiple
time series at a fine temporal scale from temporal aggregates measured on each
individual serie... | computer science |
22,495 | Constrained Maximum Correntropy Adaptive Filtering | stat.ML | Constrained adaptive filtering algorithms inculding constrained least mean
square (CLMS), constrained affine projection (CAP) and constrained recursive
least squares (CRLS) have been extensively studied in many applications. Most
existing constrained adaptive filtering algorithms are developed under mean
square error (... | computer science |
22,496 | The Generalized Reparameterization Gradient | stat.ML | The reparameterization gradient has become a widely used method to obtain
Monte Carlo gradients to optimize the variational objective. However, this
technique does not easily apply to commonly used distributions such as beta or
gamma without further approximations, and most practical applications of the
reparameterizat... | computer science |
22,497 | A nonparametric sequential test for online randomized experiments | stat.ML | We propose a nonparametric sequential test that aims to address two practical
problems pertinent to online randomized experiments: (i) how to do a hypothesis
test for complex metrics; (ii) how to prevent type $1$ error inflation under
continuous monitoring. The proposed test does not require knowledge of the
underlying... | computer science |
22,498 | Generative Adversarial Nets from a Density Ratio Estimation Perspective | stat.ML | Generative adversarial networks (GANs) are successful deep generative models.
GANs are based on a two-player minimax game. However, the objective function
derived in the original motivation is changed to obtain stronger gradients when
learning the generator. We propose a novel algorithm that repeats the density
ratio e... | computer science |
22,499 | Truncated Variational Expectation Maximization | stat.ML | We derive a novel variational expectation maximization approach based on
truncated variational distributions. Truncated distributions are proportional
to exact posteriors within a subset of a discrete state space and equal zero
otherwise. The novel variational approach is realized by first generalizing the
standard var... | computer science |
22,500 | Assisted Dictionary Learning for fMRI Data Analysis | stat.ML | Extracting information from functional magnetic resonance (fMRI) images has
been a major area of research for more than two decades. The goal of this work
is to present a new method for the analysis of fMRI data sets, that is capable
to incorporate a priori available information, via an efficient optimization
framework... | computer science |
22,501 | Statistics of Robust Optimization: A Generalized Empirical Likelihood
Approach | stat.ML | We study statistical inference and robust solution methods for stochastic
optimization problems, focusing on giving calibrated and adaptive confidence
intervals for optimal values and solutions for a range of stochastic problems.
As part of this, we develop a generalized empirical likelihood
framework---based on distri... | computer science |
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