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22,002 | Distributed parameter estimation of discrete hierarchical models via
marginal likelihoods | stat.ML | We consider discrete graphical models Markov with respect to a graph $G$ and
propose two distributed marginal methods to estimate the maximum likelihood
estimate of the canonical parameter of the model. Both methods are based on a
relaxation of the marginal likelihood obtained by considering the density of
the variable... | computer science |
22,003 | Combined l_1 and greedy l_0 penalized least squares for linear model
selection | stat.ML | We introduce a computationally effective algorithm for a linear model
selection consisting of three steps: screening--ordering--selection (SOS).
Screening of predictors is based on the thresholded Lasso that is l_1 penalized
least squares. The screened predictors are then fitted using least squares (LS)
and ordered wit... | computer science |
22,004 | Multiple Kernel Learning for Brain-Computer Interfacing | stat.ML | Combining information from different sources is a common way to improve
classification accuracy in Brain-Computer Interfacing (BCI). For instance, in
small sample settings it is useful to integrate data from other subjects or
sessions in order to improve the estimation quality of the spatial filters or
the classifier. ... | computer science |
22,005 | Efficient State-Space Inference of Periodic Latent Force Models | stat.ML | Latent force models (LFM) are principled approaches to incorporating
solutions to differential equations within non-parametric inference methods.
Unfortunately, the development and application of LFMs can be inhibited by
their computational cost, especially when closed-form solutions for the LFM are
unavailable, as is ... | computer science |
22,006 | Bayesian estimation of possible causal direction in the presence of
latent confounders using a linear non-Gaussian acyclic structural equation
model with individual-specific effects | stat.ML | We consider learning the possible causal direction of two observed variables
in the presence of latent confounding variables. Several existing methods have
been shown to consistently estimate causal direction assuming linear or some
type of nonlinear relationship and no latent confounders. However, the
estimation resul... | computer science |
22,007 | A comparison of bandwidth selectors for mean shift clustering | stat.ML | We explore the performance of several automatic bandwidth selectors,
originally designed for density gradient estimation, as data-based procedures
for nonparametric, modal clustering. The key tool to obtain a clustering from
density gradient estimators is the mean shift algorithm, which allows to obtain
a partition not... | computer science |
22,008 | Nonlinear unmixing of hyperspectral images using a semiparametric model
and spatial regularization | stat.ML | Incorporating spatial information into hyperspectral unmixing procedures has
been shown to have positive effects, due to the inherent spatial-spectral
duality in hyperspectral scenes. Current research works that consider spatial
information are mainly focused on the linear mixing model. In this paper, we
investigate a ... | computer science |
22,009 | Convergence analysis of kernel LMS algorithm with pre-tuned dictionary | stat.ML | The kernel least-mean-square (KLMS) algorithm is an appealing tool for online
identification of nonlinear systems due to its simplicity and robustness. In
addition to choosing a reproducing kernel and setting filter parameters,
designing a KLMS adaptive filter requires to select a so-called dictionary in
order to get a... | computer science |
22,010 | Joint Estimation of Multiple Graphical Models from High Dimensional Time
Series | stat.ML | In this manuscript we consider the problem of jointly estimating multiple
graphical models in high dimensions. We assume that the data are collected from
n subjects, each of which consists of T possibly dependent observations. The
graphical models of subjects vary, but are assumed to change smoothly
corresponding to a ... | computer science |
22,011 | Multivariate Generalized Gaussian Process Models | stat.ML | We propose a family of multivariate Gaussian process models for correlated
outputs, based on assuming that the likelihood function takes the generic form
of the multivariate exponential family distribution (EFD). We denote this model
as a multivariate generalized Gaussian process model, and derive Taylor and
Laplace al... | computer science |
22,012 | Stochastic Dual Coordinate Ascent with Alternating Direction Multiplier
Method | stat.ML | We propose a new stochastic dual coordinate ascent technique that can be
applied to a wide range of regularized learning problems. Our method is based
on Alternating Direction Multiplier Method (ADMM) to deal with complex
regularization functions such as structured regularizations. Although the
original ADMM is a batch... | computer science |
22,013 | Nonparametric Bayesian models of hierarchical structure in complex
networks | stat.ML | Analyzing and understanding the structure of complex relational data is
important in many applications including analysis of the connectivity in the
human brain. Such networks can have prominent patterns on different scales,
calling for a hierarchically structured model. We propose two non-parametric
Bayesian hierarchi... | computer science |
22,014 | Visualizing the Effects of a Changing Distance on Data Using Continuous
Embeddings | stat.ML | Most Machine Learning (ML) methods, from clustering to classification, rely
on a distance function to describe relationships between datapoints. For
complex datasets it is hard to avoid making some arbitrary choices when
defining a distance function. To compare images, one must choose a spatial
scale, for signals, a te... | computer science |
22,015 | The Infinite Degree Corrected Stochastic Block Model | stat.ML | In Stochastic blockmodels, which are among the most prominent statistical
models for cluster analysis of complex networks, clusters are defined as groups
of nodes with statistically similar link probabilities within and between
groups. A recent extension by Karrer and Newman incorporates a node degree
correction to mod... | computer science |
22,016 | Compressive Nonparametric Graphical Model Selection For Time Series | stat.ML | We propose a method for inferring the conditional indepen- dence graph (CIG)
of a high-dimensional discrete-time Gaus- sian vector random process from
finite-length observations. Our approach does not rely on a parametric model
(such as, e.g., an autoregressive model) for the vector random process; rather,
it only assu... | computer science |
22,017 | Signal Recovery from Pooling Representations | stat.ML | In this work we compute lower Lipschitz bounds of $\ell_p$ pooling operators
for $p=1, 2, \infty$ as well as $\ell_p$ pooling operators preceded by
half-rectification layers. These give sufficient conditions for the design of
invertible neural network layers. Numerical experiments on MNIST and image
patches confirm tha... | computer science |
22,018 | Nonparametric Bayes dynamic modeling of relational data | stat.ML | Symmetric binary matrices representing relations among entities are commonly
collected in many areas. Our focus is on dynamically evolving binary relational
matrices, with interest being in inference on the relationship structure and
prediction. We propose a nonparametric Bayesian dynamic model, which reduces
dimension... | computer science |
22,019 | Learning Pairwise Graphical Models with Nonlinear Sufficient Statistics | stat.ML | We investigate a generic problem of learning pairwise exponential family
graphical models with pairwise sufficient statistics defined by a global
mapping function, e.g., Mercer kernels. This subclass of pairwise graphical
models allow us to flexibly capture complex interactions among variables beyond
pairwise product. ... | computer science |
22,020 | Robust Vertex Classification | stat.ML | For random graphs distributed according to stochastic blockmodels, a special
case of latent position graphs, adjacency spectral embedding followed by
appropriate vertex classification is asymptotically Bayes optimal; but this
approach requires knowledge of and critically depends on the model dimension.
In this paper, w... | computer science |
22,021 | Robust Low-rank Tensor Recovery: Models and Algorithms | stat.ML | Robust tensor recovery plays an instrumental role in robustifying tensor
decompositions for multilinear data analysis against outliers, gross
corruptions and missing values and has a diverse array of applications. In this
paper, we study the problem of robust low-rank tensor recovery in a convex
optimization framework,... | computer science |
22,022 | Score-based Causal Learning in Additive Noise Models | stat.ML | Given data sampled from a number of variables, one is often interested in the
underlying causal relationships in the form of a directed acyclic graph. In the
general case, without interventions on some of the variables it is only
possible to identify the graph up to its Markov equivalence class. However, in
some situat... | computer science |
22,023 | Generalized Non-orthogonal Joint Diagonalization with LU Decomposition
and Successive Rotations | stat.ML | Non-orthogonal joint diagonalization (NJD) free of prewhitening has been
widely studied in the context of blind source separation (BSS) and array signal
processing, etc. However, NJD is used to retrieve the jointly diagonalizable
structure for a single set of target matrices which are mostly formulized with
a single da... | computer science |
22,024 | Impact of regularization on Spectral Clustering | stat.ML | The performance of spectral clustering can be considerably improved via
regularization, as demonstrated empirically in Amini et. al (2012). Here, we
provide an attempt at quantifying this improvement through theoretical
analysis. Under the stochastic block model (SBM), and its extensions, previous
results on spectral c... | computer science |
22,025 | An Algorithmic Theory of Dependent Regularizers, Part 1: Submodular
Structure | stat.ML | We present an exploration of the rich theoretical connections between several
classes of regularized models, network flows, and recent results in submodular
function theory. This work unifies key aspects of these problems under a common
theory, leading to novel methods for working with several important models of
inter... | computer science |
22,026 | Guaranteed Model Order Estimation and Sample Complexity Bounds for LDA | stat.ML | The question of how to determine the number of independent latent factors
(topics) in mixture models such as Latent Dirichlet Allocation (LDA) is of
great practical importance. In most applications, the exact number of topics is
unknown, and depends on the application and the size of the data set. Bayesian
nonparametri... | computer science |
22,027 | Every LWF and AMP chain graph originates from a set of causal models | stat.ML | This paper aims at justifying LWF and AMP chain graphs by showing that they
do not represent arbitrary independence models. Specifically, we show that
every chain graph is inclusion optimal wrt the intersection of the independence
models represented by a set of directed and acyclic graphs under conditioning.
This impli... | computer science |
22,028 | Near-optimal Anomaly Detection in Graphs using Lovasz Extended Scan
Statistic | stat.ML | The detection of anomalous activity in graphs is a statistical problem that
arises in many applications, such as network surveillance, disease outbreak
detection, and activity monitoring in social networks. Beyond its wide
applicability, graph structured anomaly detection serves as a case study in the
difficulty of bal... | computer science |
22,029 | Filtering with State-Observation Examples via Kernel Monte Carlo Filter | stat.ML | This paper addresses the problem of filtering with a state-space model.
Standard approaches for filtering assume that a probabilistic model for
observations (i.e. the observation model) is given explicitly or at least
parametrically. We consider a setting where this assumption is not satisfied;
we assume that the knowl... | computer science |
22,030 | Markov Network Structure Learning via Ensemble-of-Forests Models | stat.ML | Real world systems typically feature a variety of different dependency types
and topologies that complicate model selection for probabilistic graphical
models. We introduce the ensemble-of-forests model, a generalization of the
ensemble-of-trees model. Our model enables structure learning of Markov random
fields (MRF) ... | computer science |
22,031 | The Matrix Ridge Approximation: Algorithms and Applications | stat.ML | We are concerned with an approximation problem for a symmetric positive
semidefinite matrix due to motivation from a class of nonlinear machine
learning methods. We discuss an approximation approach that we call {matrix
ridge approximation}. In particular, we define the matrix ridge approximation
as an incomplete matri... | computer science |
22,032 | The Bernstein Function: A Unifying Framework of Nonconvex Penalization
in Sparse Estimation | stat.ML | In this paper we study nonconvex penalization using Bernstein functions.
Since the Bernstein function is concave and nonsmooth at the origin, it can
induce a class of nonconvex functions for high-dimensional sparse estimation
problems. We derive a threshold function based on the Bernstein penalty and
give its mathemati... | computer science |
22,033 | Functional Bipartite Ranking: a Wavelet-Based Filtering Approach | stat.ML | It is the main goal of this article to address the bipartite ranking issue
from the perspective of functional data analysis (FDA). Given a training set of
independent realizations of a (possibly sampled) second-order random function
with a (locally) smooth autocorrelation structure and to which a binary label
is random... | computer science |
22,034 | Detecting Parameter Symmetries in Probabilistic Models | stat.ML | Probabilistic models often have parameters that can be translated, scaled,
permuted, or otherwise transformed without changing the model. These symmetries
can lead to strong correlation and multimodality in the posterior distribution
over the model's parameters, which can pose challenges both for performing
inference a... | computer science |
22,035 | Non-parametric Bayesian modeling of complex networks | stat.ML | Modeling structure in complex networks using Bayesian non-parametrics makes
it possible to specify flexible model structures and infer the adequate model
complexity from the observed data. This paper provides a gentle introduction to
non-parametric Bayesian modeling of complex networks: Using an infinite mixture
model ... | computer science |
22,036 | Outlier robust system identification: a Bayesian kernel-based approach | stat.ML | In this paper, we propose an outlier-robust regularized kernel-based method
for linear system identification. The unknown impulse response is modeled as a
zero-mean Gaussian process whose covariance (kernel) is given by the recently
proposed stable spline kernel, which encodes information on regularity and
exponential ... | computer science |
22,037 | A model selection approach for clustering a multinomial sequence with
non-negative factorization | stat.ML | We consider a problem of clustering a sequence of multinomial observations by
way of a model selection criterion. We propose a form of a penalty term for the
model selection procedure. Our approach subsumes both the conventional AIC and
BIC criteria but also extends the conventional criteria in a way that it can be
app... | computer science |
22,038 | Probabilistic Archetypal Analysis | stat.ML | Archetypal analysis represents a set of observations as convex combinations
of pure patterns, or archetypes. The original geometric formulation of finding
archetypes by approximating the convex hull of the observations assumes them to
be real valued. This, unfortunately, is not compatible with many practical
situations... | computer science |
22,039 | A Fused Elastic Net Logistic Regression Model for Multi-Task Binary
Classification | stat.ML | Multi-task learning has shown to significantly enhance the performance of
multiple related learning tasks in a variety of situations. We present the
fused logistic regression, a sparse multi-task learning approach for binary
classification. Specifically, we introduce sparsity inducing penalties over
parameter differenc... | computer science |
22,040 | An Efficient Search Strategy for Aggregation and Discretization of
Attributes of Bayesian Networks Using Minimum Description Length | stat.ML | Bayesian networks are convenient graphical expressions for high dimensional
probability distributions representing complex relationships between a large
number of random variables. They have been employed extensively in areas such
as bioinformatics, artificial intelligence, diagnosis, and risk management. The
recovery ... | computer science |
22,041 | Exact Estimation of Multiple Directed Acyclic Graphs | stat.ML | This paper considers the problem of estimating the structure of multiple
related directed acyclic graph (DAG) models. Building on recent developments in
exact estimation of DAGs using integer linear programming (ILP), we present an
ILP approach for joint estimation over multiple DAGs, that does not require
that the ver... | computer science |
22,042 | Learning the Conditional Independence Structure of Stationary Time
Series: A Multitask Learning Approach | stat.ML | We propose a method for inferring the conditional independence graph (CIG) of
a high-dimensional Gaussian vector time series (discrete-time process) from a
finite-length observation. By contrast to existing approaches, we do not rely
on a parametric process model (such as, e.g., an autoregressive model) for the
observe... | computer science |
22,043 | Density Estimation via Discrepancy Based Adaptive Sequential Partition | stat.ML | Given $iid$ observations from an unknown absolute continuous distribution
defined on some domain $\Omega$, we propose a nonparametric method to learn a
piecewise constant function to approximate the underlying probability density
function. Our density estimate is a piecewise constant function defined on a
binary partit... | computer science |
22,044 | Tyler's Covariance Matrix Estimator in Elliptical Models with Convex
Structure | stat.ML | We address structured covariance estimation in elliptical distributions by
assuming that the covariance is a priori known to belong to a given convex set,
e.g., the set of Toeplitz or banded matrices. We consider the General Method of
Moments (GMM) optimization applied to robust Tyler's scatter M-estimator
subject to t... | computer science |
22,045 | A Permutation Approach for Selecting the Penalty Parameter in Penalized
Model Selection | stat.ML | We describe a simple, efficient, permutation based procedure for selecting
the penalty parameter in the LASSO. The procedure, which is intended for
applications where variable selection is the primary focus, can be applied in a
variety of structural settings, including generalized linear models. We briefly
discuss conn... | computer science |
22,046 | A Naive Bayes machine learning approach to risk prediction using
censored, time-to-event data | stat.ML | Predicting an individual's risk of experiencing a future clinical outcome is
a statistical task with important consequences for both practicing clinicians
and public health experts. Modern observational databases such as electronic
health records (EHRs) provide an alternative to the longitudinal cohort studies
traditio... | computer science |
22,047 | Clustering via Mode Seeking by Direct Estimation of the Gradient of a
Log-Density | stat.ML | Mean shift clustering finds the modes of the data probability density by
identifying the zero points of the density gradient. Since it does not require
to fix the number of clusters in advance, the mean shift has been a popular
clustering algorithm in various application fields. A typical implementation of
the mean shi... | computer science |
22,048 | Approximate Inference for Nonstationary Heteroscedastic Gaussian process
Regression | stat.ML | This paper presents a novel approach for approximate integration over the
uncertainty of noise and signal variances in Gaussian process (GP) regression.
Our efficient and straightforward approach can also be applied to integration
over input dependent noise variance (heteroscedasticity) and input dependent
signal varia... | computer science |
22,049 | Bayesian Reconstruction of Missing Observations | stat.ML | We focus on an interpolation method referred to Bayesian reconstruction in
this paper. Whereas in standard interpolation methods missing data are
interpolated deterministically, in Bayesian reconstruction, missing data are
interpolated probabilistically using a Bayesian treatment. In this paper, we
address the framewor... | computer science |
22,050 | High Dimensional Semiparametric Latent Graphical Model for Mixed Data | stat.ML | Graphical models are commonly used tools for modeling multivariate random
variables. While there exist many convenient multivariate distributions such as
Gaussian distribution for continuous data, mixed data with the presence of
discrete variables or a combination of both continuous and discrete variables
poses new cha... | computer science |
22,051 | Support Consistency of Direct Sparse-Change Learning in Markov Networks | stat.ML | We study the problem of learning sparse structure changes between two Markov
networks $P$ and $Q$. Rather than fitting two Markov networks separately to two
sets of data and figuring out their differences, a recent work proposed to
learn changes \emph{directly} via estimating the ratio between two Markov
network models... | computer science |
22,052 | Large scale canonical correlation analysis with iterative least squares | stat.ML | Canonical Correlation Analysis (CCA) is a widely used statistical tool with
both well established theory and favorable performance for a wide range of
machine learning problems. However, computing CCA for huge datasets can be very
slow since it involves implementing QR decomposition or singular value
decomposition of h... | computer science |
22,053 | Predictive support recovery with TV-Elastic Net penalty and logistic
regression: an application to structural MRI | stat.ML | The use of machine-learning in neuroimaging offers new perspectives in early
diagnosis and prognosis of brain diseases. Although such multivariate methods
can capture complex relationships in the data, traditional approaches provide
irregular (l2 penalty) or scattered (l1 penalty) predictive pattern with a very
limited... | computer science |
22,054 | Resolution-limit-free and local Non-negative Matrix Factorization
quality functions for graph clustering | stat.ML | Many graph clustering quality functions suffer from a resolution limit, the
inability to find small clusters in large graphs. So called
resolution-limit-free quality functions do not have this limit. This property
was previously introduced for hard clustering, that is, graph partitioning.
We investigate the resolutio... | computer science |
22,055 | Efficient Bayesian Nonparametric Modelling of Structured Point Processes | stat.ML | This paper presents a Bayesian generative model for dependent Cox point
processes, alongside an efficient inference scheme which scales as if the point
processes were modelled independently. We can handle missing data naturally,
infer latent structure, and cope with large numbers of observed processes. A
further novel ... | computer science |
22,056 | Understanding Random Forests: From Theory to Practice | stat.ML | Data analysis and machine learning have become an integrative part of the
modern scientific methodology, offering automated procedures for the prediction
of a phenomenon based on past observations, unraveling underlying patterns in
data and providing insights about the problem. Yet, caution should avoid using
machine l... | computer science |
22,057 | Bayesian Probabilistic Matrix Factorization: A User Frequency Analysis | stat.ML | Matrix factorization (MF) has become a common approach to collaborative
filtering, due to ease of implementation and scalability to large data sets.
Two existing drawbacks of the basic model is that it does not incorporate side
information on either users or items, and assumes a common variance for all
users. We extend... | computer science |
22,058 | Automated Machine Learning on Big Data using Stochastic Algorithm Tuning | stat.ML | We introduce a means of automating machine learning (ML) for big data tasks,
by performing scalable stochastic Bayesian optimisation of ML algorithm
parameters and hyper-parameters. More often than not, the critical tuning of ML
algorithm parameters has relied on domain expertise from experts, along with
laborious hand... | computer science |
22,059 | Consistency and fluctuations for stochastic gradient Langevin dynamics | stat.ML | Applying standard Markov chain Monte Carlo (MCMC) algorithms to large data
sets is computationally expensive. Both the calculation of the acceptance
probability and the creation of informed proposals usually require an iteration
through the whole data set. The recently proposed stochastic gradient Langevin
dynamics (SG... | computer science |
22,060 | Context-specific independence in graphical log-linear models | stat.ML | Log-linear models are the popular workhorses of analyzing contingency tables.
A log-linear parameterization of an interaction model can be more expressive
than a direct parameterization based on probabilities, leading to a powerful
way of defining restrictions derived from marginal, conditional and
context-specific ind... | computer science |
22,061 | Scalable Bayesian Modelling of Paired Symbols | stat.ML | We present a novel, scalable and Bayesian approach to modelling the
occurrence of pairs of symbols (i,j) drawn from a large vocabulary. Observed
pairs are assumed to be generated by a simple popularity based selection
process followed by censoring using a preference function. By basing inference
on the well-founded pri... | computer science |
22,062 | Sparse Estimation with Strongly Correlated Variables using Ordered
Weighted L1 Regularization | stat.ML | This paper studies ordered weighted L1 (OWL) norm regularization for sparse
estimation problems with strongly correlated variables. We prove sufficient
conditions for clustering based on the correlation/colinearity of variables
using the OWL norm, of which the so-called OSCAR is a particular case. Our
results extend pr... | computer science |
22,063 | Raiders of the Lost Architecture: Kernels for Bayesian Optimization in
Conditional Parameter Spaces | stat.ML | In practical Bayesian optimization, we must often search over structures with
differing numbers of parameters. For instance, we may wish to search over
neural network architectures with an unknown number of layers. To relate
performance data gathered for different architectures, we define a new kernel
for conditional p... | computer science |
22,064 | Probabilistic Network Metrics: Variational Bayesian Network Centrality | stat.ML | Network metrics form a fundamental part of the network analysis toolbox. Used
to quantitatively measure different aspects of the network, these metrics can
give insights into the underlying network structure and function. In this work,
we connect network metrics to modern probabilistic machine learning. We focus
on the... | computer science |
22,065 | The Randomized Causation Coefficient | stat.ML | We are interested in learning causal relationships between pairs of random
variables, purely from observational data. To effectively address this task,
the state-of-the-art relies on strong assumptions regarding the mechanisms
mapping causes to effects, such as invertibility or the existence of additive
noise, which on... | computer science |
22,066 | Non-linear Causal Inference using Gaussianity Measures | stat.ML | We provide theoretical and empirical evidence for a type of asymmetry between
causes and effects that is present when these are related via linear models
contaminated with additive non-Gaussian noise. Assuming that the causes and the
effects have the same distribution, we show that the distribution of the
residuals of ... | computer science |
22,067 | Expectation Propagation | stat.ML | Variational inference is a powerful concept that underlies many iterative
approximation algorithms; expectation propagation, mean-field methods and
belief propagations were all central themes at the school that can be perceived
from this unifying framework. The lectures of Manfred Opper introduce the
archetypal example... | computer science |
22,068 | Deconvolution of High-Dimensional Mixtures via Boosting, with
Application to Diffusion-Weighted MRI of Human Brain | stat.ML | Diffusion-weighted magnetic resonance imaging (DWI) and fiber tractography
are the only methods to measure the structure of the white matter in the living
human brain. The diffusion signal has been modelled as the combined
contribution from many individual fascicles of nerve fibers passing through
each location in the ... | computer science |
22,069 | MIST: L0 Sparse Linear Regression with Momentum | stat.ML | Significant attention has been given to minimizing a penalized least squares
criterion for estimating sparse solutions to large linear systems of equations.
The penalty is responsible for inducing sparsity and the natural choice is the
so-called $l_0$ norm. In this paper we develop a Momentumized Iterative
Shrinkage Th... | computer science |
22,070 | Unsupervised Bump Hunting Using Principal Components | stat.ML | Principal Components Analysis is a widely used technique for dimension
reduction and characterization of variability in multivariate populations. Our
interest lies in studying when and why the rotation to principal components can
be used effectively within a response-predictor set relationship in the context
of mode hu... | computer science |
22,071 | Linear State-Space Model with Time-Varying Dynamics | stat.ML | This paper introduces a linear state-space model with time-varying dynamics.
The time dependency is obtained by forming the state dynamics matrix as a
time-varying linear combination of a set of matrices. The time dependency of
the weights in the linear combination is modelled by another linear Gaussian
dynamical model... | computer science |
22,072 | Individualized Rank Aggregation using Nuclear Norm Regularization | stat.ML | In recent years rank aggregation has received significant attention from the
machine learning community. The goal of such a problem is to combine the
(partially revealed) preferences over objects of a large population into a
single, relatively consistent ordering of those objects. However, in many
cases, we might not w... | computer science |
22,073 | Graphical LASSO Based Model Selection for Time Series | stat.ML | We propose a novel graphical model selection (GMS) scheme for
high-dimensional stationary time series or discrete time process. The method is
based on a natural generalization of the graphical LASSO (gLASSO), introduced
originally for GMS based on i.i.d. samples, and estimates the conditional
independence graph (CIG) o... | computer science |
22,074 | Distributed Estimation, Information Loss and Exponential Families | stat.ML | Distributed learning of probabilistic models from multiple data repositories
with minimum communication is increasingly important. We study a simple
communication-efficient learning framework that first calculates the local
maximum likelihood estimates (MLE) based on the data subsets, and then combines
the local MLEs t... | computer science |
22,075 | Learning without Concentration for General Loss Functions | stat.ML | We study prediction and estimation problems using empirical risk
minimization, relative to a general convex loss function. We obtain sharp error
rates even when concentration is false or is very restricted, for example, in
heavy-tailed scenarios. Our results show that the error rate depends on two
parameters: one captu... | computer science |
22,076 | Convex Modeling of Interactions with Strong Heredity | stat.ML | We consider the task of fitting a regression model involving interactions
among a potentially large set of covariates, in which we wish to enforce strong
heredity. We propose FAMILY, a very general framework for this task. Our
proposal is a generalization of several existing methods, such as VANISH
[Radchenko and James... | computer science |
22,077 | Variational Reformulation of Bayesian Inverse Problems | stat.ML | The classical approach to inverse problems is based on the optimization of a
misfit function. Despite its computational appeal, such an approach suffers
from many shortcomings, e.g., non-uniqueness of solutions, modeling prior
knowledge, etc. The Bayesian formalism to inverse problems avoids most of the
difficulties en... | computer science |
22,078 | A General Stochastic Algorithmic Framework for Minimizing Expensive
Black Box Objective Functions Based on Surrogate Models and Sensitivity
Analysis | stat.ML | We are focusing on bound constrained global optimization problems, whose
objective functions are computationally expensive black-box functions and have
multiple local minima. The recently popular Metric Stochastic Response Surface
(MSRS) algorithm proposed by \cite{Regis2007SRBF} based on adaptive or
sequential learnin... | computer science |
22,079 | Bayesian Manifold Learning: The Locally Linear Latent Variable Model
(LL-LVM) | stat.ML | We introduce the Locally Linear Latent Variable Model (LL-LVM), a
probabilistic model for non-linear manifold discovery that describes a joint
distribution over observations, their manifold coordinates and locally linear
maps conditioned on a set of neighbourhood relationships. The model allows
straightforward variatio... | computer science |
22,080 | Scalable Nonparametric Bayesian Inference on Point Processes with
Gaussian Processes | stat.ML | In this paper we propose the first non-parametric Bayesian model using
Gaussian Processes to make inference on Poisson Point Processes without
resorting to gridding the domain or to introducing latent thinning points.
Unlike competing models that scale cubically and have a squared memory
requirement in the number of da... | computer science |
22,081 | An Aggregation Method for Sparse Logistic Regression | stat.ML | $L_1$ regularized logistic regression has now become a workhorse of data
mining and bioinformatics: it is widely used for many classification problems,
particularly ones with many features. However, $L_1$ regularization typically
selects too many features and that so-called false positives are unavoidable.
In this pape... | computer science |
22,082 | Fully Automated Myocardial Infarction Classification using Ordinary
Differential Equations | stat.ML | Portable, Wearable and Wireless electrocardiogram (ECG) Systems have the
potential to be used as point-of-care for cardiovascular disease diagnostic
systems. Such wearable and wireless ECG systems require automatic detection of
cardiovascular disease. Even in the primary care, automation of ECG diagnostic
systems will ... | computer science |
22,083 | A Novel Statistical Method Based on Dynamic Models for Classification | stat.ML | Realizations of stochastic process are often observed temporal data or
functional data. There are growing interests in classification of dynamic or
functional data. The basic feature of functional data is that the functional
data have infinite dimensions and are highly correlated. An essential issue for
classifying dyn... | computer science |
22,084 | Sensitivity Analysis for Computationally Expensive Models using
Optimization and Objective-oriented Surrogate Approximations | stat.ML | In this paper, we focus on developing efficient sensitivity analysis methods
for a computationally expensive objective function $f(x)$ in the case that the
minimization of it has just been performed. Here "computationally expensive"
means that each of its evaluation takes significant amount of time, and
therefore our m... | computer science |
22,085 | Multiple Output Regression with Latent Noise | stat.ML | In high-dimensional data, structured noise caused by observed and unobserved
factors affecting multiple target variables simultaneously, imposes a serious
challenge for modeling, by masking the often weak signal. Therefore, (1)
explaining away the structured noise in multiple-output regression is of
paramount importanc... | computer science |
22,086 | A General Statistic Framework for Genome-based Disease Risk Prediction | stat.ML | Advances of modern sensing and sequencing technologies generate a deluge of
high dimensional space-temporal physiological and next-generation sequencing
(NGS) data. Physiological traits are observed either as continuous random
functions, or on a dense grid and referred to as function-valued traits. Both
physiological a... | computer science |
22,087 | A Ternary Non-Commutative Latent Factor Model for Scalable Three-Way
Real Tensor Completion | stat.ML | Motivated by large-scale Collaborative-Filtering applications, we present a
Non-Commuting Latent Factor (NCLF) tensor-completion approach for modeling
three-way arrays, which is diagonal like the standard PARAFAC, but wherein
different terms distinguish different kinds of three-way relations of
co-clusters, as determin... | computer science |
22,088 | Two New Approaches to Compressed Sensing Exhibiting Both Robust Sparse
Recovery and the Grouping Effect | stat.ML | In this paper we introduce a new optimization formulation for sparse
regression and compressed sensing, called CLOT (Combined L-One and Two),
wherein the regularizer is a convex combination of the $\ell_1$- and
$\ell_2$-norms. This formulation differs from the Elastic Net (EN) formulation,
in which the regularizer is a... | computer science |
22,089 | On Estimating $L_2^2$ Divergence | stat.ML | We give a comprehensive theoretical characterization of a nonparametric
estimator for the $L_2^2$ divergence between two continuous distributions. We
first bound the rate of convergence of our estimator, showing that it is
$\sqrt{n}$-consistent provided the densities are sufficiently smooth. In this
smooth regime, we t... | computer science |
22,090 | Learning Mixed Multinomial Logit Model from Ordinal Data | stat.ML | Motivated by generating personalized recommendations using ordinal (or
preference) data, we study the question of learning a mixture of MultiNomial
Logit (MNL) model, a parameterized class of distributions over permutations,
from partial ordinal or preference data (e.g. pair-wise comparisons). Despite
its long standing... | computer science |
22,091 | Variational Inference for Gaussian Process Modulated Poisson Processes | stat.ML | We present the first fully variational Bayesian inference scheme for
continuous Gaussian-process-modulated Poisson processes. Such point processes
are used in a variety of domains, including neuroscience, geo-statistics and
astronomy, but their use is hindered by the computational cost of existing
inference schemes. Ou... | computer science |
22,092 | Sampling for Inference in Probabilistic Models with Fast Bayesian
Quadrature | stat.ML | We propose a novel sampling framework for inference in probabilistic models:
an active learning approach that converges more quickly (in wall-clock time)
than Markov chain Monte Carlo (MCMC) benchmarks. The central challenge in
probabilistic inference is numerical integration, to average over ensembles of
models or unk... | computer science |
22,093 | A Nonparametric Adaptive Nonlinear Statistical Filter | stat.ML | We use statistical learning methods to construct an adaptive state estimator
for nonlinear stochastic systems. Optimal state estimation, in the form of a
Kalman filter, requires knowledge of the system's process and measurement
uncertainty. We propose that these uncertainties can be estimated from
(conditioned on) past... | computer science |
22,094 | Simple approximate MAP Inference for Dirichlet processes | stat.ML | The Dirichlet process mixture (DPM) is a ubiquitous, flexible Bayesian
nonparametric statistical model. However, full probabilistic inference in this
model is analytically intractable, so that computationally intensive techniques
such as Gibb's sampling are required. As a result, DPM-based methods, which
have considera... | computer science |
22,095 | Proof Supplement - Learning Sparse Causal Models is not NP-hard
(UAI2013) | stat.ML | This article contains detailed proofs and additional examples related to the
UAI-2013 submission `Learning Sparse Causal Models is not NP-hard'. It
describes the FCI+ algorithm: a method for sound and complete causal model
discovery in the presence of latent confounders and/or selection bias, that has
worst case polyno... | computer science |
22,096 | Stochastic Variational Inference for Hidden Markov Models | stat.ML | Variational inference algorithms have proven successful for Bayesian analysis
in large data settings, with recent advances using stochastic variational
inference (SVI). However, such methods have largely been studied in independent
or exchangeable data settings. We develop an SVI algorithm to learn the
parameters of hi... | computer science |
22,097 | Sublinear-Time Approximate MCMC Transitions for Probabilistic Programs | stat.ML | Probabilistic programming languages can simplify the development of machine
learning techniques, but only if inference is sufficiently scalable.
Unfortunately, Bayesian parameter estimation for highly coupled models such as
regressions and state-space models still scales poorly; each MCMC transition
takes linear time i... | computer science |
22,098 | Scalable Variational Gaussian Process Classification | stat.ML | Gaussian process classification is a popular method with a number of
appealing properties. We show how to scale the model within a variational
inducing point framework, outperforming the state of the art on benchmark
datasets. Importantly, the variational formulation can be exploited to allow
classification in problems... | computer science |
22,099 | Parameter estimation in spherical symmetry groups | stat.ML | This paper considers statistical estimation problems where the probability
distribution of the observed random variable is invariant with respect to
actions of a finite topological group. It is shown that any such distribution
must satisfy a restricted finite mixture representation. When specialized to
the case of dist... | computer science |
22,100 | Stochastic Compositional Gradient Descent: Algorithms for Minimizing
Compositions of Expected-Value Functions | stat.ML | Classical stochastic gradient methods are well suited for minimizing
expected-value objective functions. However, they do not apply to the
minimization of a nonlinear function involving expected values or a composition
of two expected-value functions, i.e., problems of the form $\min_x
\mathbf{E}_v [f_v\big(\mathbf{E}_... | computer science |
22,101 | Causal Inference by Identification of Vector Autoregressive Processes
with Hidden Components | stat.ML | A widely applied approach to causal inference from a non-experimental time
series $X$, often referred to as "(linear) Granger causal analysis", is to
regress present on past and interpret the regression matrix $\hat{B}$ causally.
However, if there is an unmeasured time series $Z$ that influences $X$, then
this approach... | computer science |
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