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22,302 | Private Causal Inference | stat.ML | Causal inference deals with identifying which random variables "cause" or
control other random variables. Recent advances on the topic of causal
inference based on tools from statistical estimation and machine learning have
resulted in practical algorithms for causal inference. Causal inference has the
potential to hav... | computer science |
22,303 | Classification of weak multi-view signals by sharing factors in a
mixture of Bayesian group factor analyzers | stat.ML | We propose a novel classification model for weak signal data, building upon a
recent model for Bayesian multi-view learning, Group Factor Analysis (GFA).
Instead of assuming all data to come from a single GFA model, we allow latent
clusters, each having a different GFA model and producing a different class
distribution... | computer science |
22,304 | Oracle inequalities for ranking and U-processes with Lasso penalty | stat.ML | We investigate properties of estimators obtained by minimization of
U-processes with the Lasso penalty in high-dimensional settings. Our attention
is focused on the ranking problem that is popular in machine learning. It is
related to guessing the ordering between objects on the basis of their observed
predictors. We p... | computer science |
22,305 | Expectation propagation for continuous time stochastic processes | stat.ML | We consider the inverse problem of reconstructing the posterior measure over
the trajec- tories of a diffusion process from discrete time observations and
continuous time constraints. We cast the problem in a Bayesian framework and
derive approximations to the posterior distributions of single time marginals
using vari... | computer science |
22,306 | k-Means Clustering Is Matrix Factorization | stat.ML | We show that the objective function of conventional k-means clustering can be
expressed as the Frobenius norm of the difference of a data matrix and a low
rank approximation of that data matrix. In short, we show that k-means
clustering is a matrix factorization problem. These notes are meant as a
reference and intende... | computer science |
22,307 | High-Order Stochastic Gradient Thermostats for Bayesian Learning of Deep
Models | stat.ML | Learning in deep models using Bayesian methods has generated significant
attention recently. This is largely because of the feasibility of modern
Bayesian methods to yield scalable learning and inference, while maintaining a
measure of uncertainty in the model parameters. Stochastic gradient MCMC
algorithms (SG-MCMC) a... | computer science |
22,308 | Preconditioned Stochastic Gradient Langevin Dynamics for Deep Neural
Networks | stat.ML | Effective training of deep neural networks suffers from two main issues. The
first is that the parameter spaces of these models exhibit pathological
curvature. Recent methods address this problem by using adaptive
preconditioning for Stochastic Gradient Descent (SGD). These methods improve
convergence by adapting to th... | computer science |
22,309 | Histogram Meets Topic Model: Density Estimation by Mixture of Histograms | stat.ML | The histogram method is a powerful non-parametric approach for estimating the
probability density function of a continuous variable. But the construction of
a histogram, compared to the parametric approaches, demands a large number of
observations to capture the underlying density function. Thus it is not
suitable for ... | computer science |
22,310 | Post-Regularization Inference for Time-Varying Nonparanormal Graphical
Models | stat.ML | We propose a novel class of time-varying nonparanormal graphical models,
which allows us to model high dimensional heavy-tailed systems and the
evolution of their latent network structures. Under this model, we develop
statistical tests for presence of edges both locally at a fixed index value and
globally over a range... | computer science |
22,311 | Testing for Differences in Gaussian Graphical Models: Applications to
Brain Connectivity | stat.ML | Functional brain networks are well described and estimated from data with
Gaussian Graphical Models (GGMs), e.g. using sparse inverse covariance
estimators. Comparing functional connectivity of subjects in two populations
calls for comparing these estimated GGMs. Our goal is to identify differences
in GGMs known to hav... | computer science |
22,312 | Error Bounds for Compressed Sensing Algorithms With Group Sparsity: A
Unified Approach | stat.ML | In compressed sensing, in order to recover a sparse or nearly sparse vector
from possibly noisy measurements, the most popular approach is $\ell_1$-norm
minimization. Upper bounds for the $\ell_2$- norm of the error between the true
and estimated vectors are given in [1] and reviewed in [2], while bounds for
the $\ell_... | computer science |
22,313 | Sharp Computational-Statistical Phase Transitions via Oracle
Computational Model | stat.ML | We study the fundamental tradeoffs between computational tractability and
statistical accuracy for a general family of hypothesis testing problems with
combinatorial structures. Based upon an oracle model of computation, which
captures the interactions between algorithms and data, we establish a general
lower bound tha... | computer science |
22,314 | Joint Estimation of Precision Matrices in Heterogeneous Populations | stat.ML | We introduce a general framework for estimation of inverse covariance, or
precision, matrices from heterogeneous populations. The proposed framework uses
a Laplacian shrinkage penalty to encourage similarity among estimates from
disparate, but related, subpopulations, while allowing for differences among
matrices. We p... | computer science |
22,315 | Learning relationships between data obtained independently | stat.ML | The aim of this paper is to provide a new method for learning the
relationships between data that have been obtained independently. Unlike
existing methods like matching, the proposed technique does not require any
contextual information, provided that the dependency between the variables of
interest is monotone. It ca... | computer science |
22,316 | On Bayesian index policies for sequential resource allocation | stat.ML | This paper is about index policies for minimizing (frequentist) regret in a
stochastic multi-armed bandit model, inspired by a Bayesian view on the
problem. Our main contribution is to prove that the Bayes-UCB algorithm, which
relies on quantiles of posterior distributions, is asymptotically optimal when
the reward dis... | computer science |
22,317 | Numerical Coding of Nominal Data | stat.ML | In this paper, a novel approach for coding nominal data is proposed. For the
given nominal data, a rank in a form of complex number is assigned. The
proposed method does not lose any information about the attribute and brings
other properties previously unknown. The approach based on these knew
properties can been used... | computer science |
22,318 | A Framework for Individualizing Predictions of Disease Trajectories by
Exploiting Multi-Resolution Structure | stat.ML | For many complex diseases, there is a wide variety of ways in which an
individual can manifest the disease. The challenge of personalized medicine is
to develop tools that can accurately predict the trajectory of an individual's
disease, which can in turn enable clinicians to optimize treatments. We
represent an indivi... | computer science |
22,319 | Nonlinear variable selection with continuous outcome: a nonparametric
incremental forward stagewise approach | stat.ML | We present a method of variable selection for the situation where some
predictors are nonlinearly associated with a continuous outcome variable. The
method doesn't assume any specific functional form, and can select from a large
number of candidates. It takes the form of incremental forward stagewise
regression, in whi... | computer science |
22,320 | Non-Gaussian Component Analysis with Log-Density Gradient Estimation | stat.ML | Non-Gaussian component analysis (NGCA) is aimed at identifying a linear
subspace such that the projected data follows a non-Gaussian distribution. In
this paper, we propose a novel NGCA algorithm based on log-density gradient
estimation. Unlike existing methods, the proposed NGCA algorithm identifies the
linear subspac... | computer science |
22,321 | Principal Polynomial Analysis | stat.ML | This paper presents a new framework for manifold learning based on a sequence
of principal polynomials that capture the possibly nonlinear nature of the
data. The proposed Principal Polynomial Analysis (PPA) generalizes PCA by
modeling the directions of maximal variance by means of curves, instead of
straight lines. Co... | computer science |
22,322 | Iterative Gaussianization: from ICA to Random Rotations | stat.ML | Most signal processing problems involve the challenging task of
multidimensional probability density function (PDF) estimation. In this work,
we propose a solution to this problem by using a family of Rotation-based
Iterative Gaussianization (RBIG) transforms. The general framework consists of
the sequential applicatio... | computer science |
22,323 | DOLDA - a regularized supervised topic model for high-dimensional
multi-class regression | stat.ML | Generating user interpretable multi-class predictions in data rich
environments with many classes and explanatory covariates is a daunting task.
We introduce Diagonal Orthant Latent Dirichlet Allocation (DOLDA), a supervised
topic model for multi-class classification that can handle both many classes as
well as many co... | computer science |
22,324 | Semi-supervised K-means++ | stat.ML | Traditionally, practitioners initialize the {\tt k-means} algorithm with
centers chosen uniformly at random. Randomized initialization with uneven
weights ({\tt k-means++}) has recently been used to improve the performance
over this strategy in cost and run-time. We consider the k-means problem with
semi-supervised inf... | computer science |
22,325 | High-Dimensional Regularized Discriminant Analysis | stat.ML | Regularized discriminant analysis (RDA), proposed by Friedman (1989), is a
widely popular classifier that lacks interpretability and is impractical for
high-dimensional data sets. Here, we present an interpretable and
computationally efficient classifier called high-dimensional RDA (HDRDA),
designed for the small-sampl... | computer science |
22,326 | A Probabilistic Modeling Approach to Hearing Loss Compensation | stat.ML | Hearing Aid (HA) algorithms need to be tuned ("fitted") to match the
impairment of each specific patient. The lack of a fundamental HA fitting
theory is a strong contributing factor to an unsatisfying sound experience for
about 20% of hearing aid patients. This paper proposes a probabilistic modeling
approach to the de... | computer science |
22,327 | Simultaneous Safe Screening of Features and Samples in Doubly Sparse
Modeling | stat.ML | The problem of learning a sparse model is conceptually interpreted as the
process of identifying active features/samples and then optimizing the model
over them. Recently introduced safe screening allows us to identify a part of
non-active features/samples. So far, safe screening has been individually
studied either fo... | computer science |
22,328 | A Kernel Test of Goodness of Fit | stat.ML | We propose a nonparametric statistical test for goodness-of-fit: given a set
of samples, the test determines how likely it is that these were generated from
a target density function. The measure of goodness-of-fit is a divergence
constructed via Stein's method using functions from a Reproducing Kernel
Hilbert Space. O... | computer science |
22,329 | Bayesian nonparametric image segmentation using a generalized
Swendsen-Wang algorithm | stat.ML | Unsupervised image segmentation aims at clustering the set of pixels of an
image into spatially homogeneous regions. We introduce here a class of Bayesian
nonparametric models to address this problem. These models are based on a
combination of a Potts-like spatial smoothness component and a prior on
partitions which is... | computer science |
22,330 | A Kernelized Stein Discrepancy for Goodness-of-fit Tests and Model
Evaluation | stat.ML | We derive a new discrepancy statistic for measuring differences between two
probability distributions based on combining Stein's identity with the
reproducing kernel Hilbert space theory. We apply our result to test how well a
probabilistic model fits a set of observations, and derive a new class of
powerful goodness-o... | computer science |
22,331 | Stochastic Quasi-Newton Langevin Monte Carlo | stat.ML | Recently, Stochastic Gradient Markov Chain Monte Carlo (SG-MCMC) methods have
been proposed for scaling up Monte Carlo computations to large data problems.
Whilst these approaches have proven useful in many applications, vanilla
SG-MCMC might suffer from poor mixing rates when random variables exhibit
strong couplings ... | computer science |
22,332 | Online Low-Rank Subspace Learning from Incomplete Data: A Bayesian View | stat.ML | Extracting the underlying low-dimensional space where high-dimensional
signals often reside has long been at the center of numerous algorithms in the
signal processing and machine learning literature during the past few decades.
At the same time, working with incomplete (partly observed) large scale
datasets has recent... | computer science |
22,333 | A Universal Approximation Theorem for Mixture of Experts Models | stat.ML | The mixture of experts (MoE) model is a popular neural network architecture
for nonlinear regression and classification. The class of MoE mean functions is
known to be uniformly convergent to any unknown target function, assuming that
the target function is from Sobolev space that is sufficiently differentiable
and tha... | computer science |
22,334 | Safe Pattern Pruning: An Efficient Approach for Predictive Pattern
Mining | stat.ML | In this paper we study predictive pattern mining problems where the goal is
to construct a predictive model based on a subset of predictive patterns in the
database. Our main contribution is to introduce a novel method called safe
pattern pruning (SPP) for a class of predictive pattern mining problems. The
SPP method a... | computer science |
22,335 | Selective Inference Approach for Statistically Sound Predictive Pattern
Mining | stat.ML | Discovering statistically significant patterns from databases is an important
challenging problem. The main obstacle of this problem is in the difficulty of
taking into account the selection bias, i.e., the bias arising from the fact
that patterns are selected from extremely large number of candidates in
databases. In ... | computer science |
22,336 | The Multivariate Generalised von Mises distribution: Inference and
applications | stat.ML | Circular variables arise in a multitude of data-modelling contexts ranging
from robotics to the social sciences, but they have been largely overlooked by
the machine learning community. This paper partially redresses this imbalance
by extending some standard probabilistic modelling tools to the circular
domain. First w... | computer science |
22,337 | Patterns of Scalable Bayesian Inference | stat.ML | Datasets are growing not just in size but in complexity, creating a demand
for rich models and quantification of uncertainty. Bayesian methods are an
excellent fit for this demand, but scaling Bayesian inference is a challenge.
In response to this challenge, there has been considerable recent work based on
varying assu... | computer science |
22,338 | Robust Kernel (Cross-) Covariance Operators in Reproducing Kernel
Hilbert Space toward Kernel Methods | stat.ML | To the best of our knowledge, there are no general well-founded robust
methods for statistical unsupervised learning. Most of the unsupervised methods
explicitly or implicitly depend on the kernel covariance operator (kernel CO)
or kernel cross-covariance operator (kernel CCO). They are sensitive to
contaminated data, ... | computer science |
22,339 | What is the distribution of the number of unique original items in a
bootstrap sample? | stat.ML | Sampling with replacement occurs in many settings in machine learning,
notably in the bagging ensemble technique and the .632+ validation scheme. The
number of unique original items in a bootstrap sample can have an important
role in the behaviour of prediction models learned on it. Indeed, there are
uncontrived exampl... | computer science |
22,340 | Scaling up Dynamic Topic Models | stat.ML | Dynamic topic models (DTMs) are very effective in discovering topics and
capturing their evolution trends in time series data. To do posterior inference
of DTMs, existing methods are all batch algorithms that scan the full dataset
before each update of the model and make inexact variational approximations
with mean-fie... | computer science |
22,341 | A Mutual Contamination Analysis of Mixed Membership and Partial Label
Models | stat.ML | Many machine learning problems can be characterized by mutual contamination
models. In these problems, one observes several random samples from different
convex combinations of a set of unknown base distributions. It is of interest
to decontaminate mutual contamination models, i.e., to recover the base
distributions ei... | computer science |
22,342 | The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM | stat.ML | We propose the segmented iHMM (siHMM), a hierarchical infinite hidden Markov
model (iHMM) that supports a simple, efficient inference scheme. The siHMM is
well suited to segmentation problems, where the goal is to identify points at
which a time series transitions from one relatively stable regime to a new
regime. Conv... | computer science |
22,343 | Inference Networks for Sequential Monte Carlo in Graphical Models | stat.ML | We introduce a new approach for amortizing inference in directed graphical
models by learning heuristic approximations to stochastic inverses, designed
specifically for use as proposal distributions in sequential Monte Carlo
methods. We describe a procedure for constructing and learning a structured
neural network whic... | computer science |
22,344 | A Simple Approach to Sparse Clustering | stat.ML | Consider the problem of sparse clustering, where it is assumed that only a
subset of the features are useful for clustering purposes. In the framework of
the COSA method of Friedman and Meulman, subsequently improved in the form of
the Sparse K-means method of Witten and Tibshirani, a natural and simpler
hill-climbing ... | computer science |
22,345 | Multivariate Hawkes Processes for Large-scale Inference | stat.ML | In this paper, we present a framework for fitting multivariate Hawkes
processes for large-scale problems both in the number of events in the observed
history $n$ and the number of event types $d$ (i.e. dimensions). The proposed
Low-Rank Hawkes Process (LRHP) framework introduces a low-rank approximation of
the kernel m... | computer science |
22,346 | Multi-Information Source Optimization | stat.ML | We consider Bayesian optimization of an expensive-to-evaluate black-box
objective function, where we also have access to cheaper approximations of the
objective. In general, such approximations arise in applications such as
reinforcement learning, engineering, and the natural sciences, and are subject
to an inherent, u... | computer science |
22,347 | A Kernel Test for Three-Variable Interactions with Random Processes | stat.ML | We apply a wild bootstrap method to the Lancaster three-variable interaction
measure in order to detect factorisation of the joint distribution on three
variables forming a stationary random process, for which the existing
permutation bootstrap method fails. As in the i.i.d. case, the Lancaster test
is found to outperf... | computer science |
22,348 | Whitening-Free Least-Squares Non-Gaussian Component Analysis | stat.ML | Non-Gaussian component analysis (NGCA) is an unsupervised linear dimension
reduction method that extracts low-dimensional non-Gaussian "signals" from
high-dimensional data contaminated with Gaussian noise. NGCA can be regarded as
a generalization of projection pursuit (PP) and independent component analysis
(ICA) to mu... | computer science |
22,349 | Overdispersed Black-Box Variational Inference | stat.ML | We introduce overdispersed black-box variational inference, a method to
reduce the variance of the Monte Carlo estimator of the gradient in black-box
variational inference. Instead of taking samples from the variational
distribution, we use importance sampling to take samples from an overdispersed
distribution in the s... | computer science |
22,350 | Partition Functions from Rao-Blackwellized Tempered Sampling | stat.ML | Partition functions of probability distributions are important quantities for
model evaluation and comparisons. We present a new method to compute partition
functions of complex and multimodal distributions. Such distributions are often
sampled using simulated tempering, which augments the target space with an
auxiliar... | computer science |
22,351 | Bayesian Learning of Kernel Embeddings | stat.ML | Kernel methods are one of the mainstays of machine learning, but the problem
of kernel learning remains challenging, with only a few heuristics and very
little theory. This is of particular importance in methods based on estimation
of kernel mean embeddings of probability measures. For characteristic kernels,
which inc... | computer science |
22,352 | Note on the equivalence of hierarchical variational models and auxiliary
deep generative models | stat.ML | This note compares two recently published machine learning methods for
constructing flexible, but tractable families of variational hidden-variable
posteriors. The first method, called "hierarchical variational models" enriches
the inference model with an extra variable, while the other, called "auxiliary
deep generati... | computer science |
22,353 | Computing AIC for black-box models using Generalised Degrees of Freedom:
a comparison with cross-validation | stat.ML | Generalised Degrees of Freedom (GDF), as defined by Ye (1998 JASA
93:120-131), represent the sensitivity of model fits to perturbations of the
data. As such they can be computed for any statistical model, making it
possible, in principle, to derive the number of parameters in machine-learning
approaches. Defined origin... | computer science |
22,354 | Square Root Graphical Models: Multivariate Generalizations of Univariate
Exponential Families that Permit Positive Dependencies | stat.ML | We develop Square Root Graphical Models (SQR), a novel class of parametric
graphical models that provides multivariate generalizations of univariate
exponential family distributions. Previous multivariate graphical models [Yang
et al. 2015] did not allow positive dependencies for the exponential and
Poisson generalizat... | computer science |
22,355 | Median-Truncated Nonconvex Approach for Phase Retrieval with Outliers | stat.ML | This paper investigates the phase retrieval problem, which aims to recover a
signal from the magnitudes of its linear measurements. We develop statistically
and computationally efficient algorithms for the situation when the
measurements are corrupted by sparse outliers that can take arbitrary values.
We propose a nove... | computer science |
22,356 | Laplacian Eigenmaps from Sparse, Noisy Similarity Measurements | stat.ML | Manifold learning and dimensionality reduction techniques are ubiquitous in
science and engineering, but can be computationally expensive procedures when
applied to large data sets or when similarities are expensive to compute. To
date, little work has been done to investigate the tradeoff between
computational resourc... | computer science |
22,357 | A ranking approach to global optimization | stat.ML | We consider the problem of maximizing an unknown function over a compact and
convex set using as few observations as possible. We observe that the
optimization of the function essentially relies on learning the induced
bipartite ranking rule of f. Based on this idea, we relate global optimization
to bipartite ranking w... | computer science |
22,358 | Short-term time series prediction using Hilbert space embeddings of
autoregressive processes | stat.ML | Linear autoregressive models serve as basic representations of discrete time
stochastic processes. Different attempts have been made to provide non-linear
versions of the basic autoregressive process, including different versions
based on kernel methods. Motivated by the powerful framework of Hilbert space
embeddings o... | computer science |
22,359 | Convergence of Contrastive Divergence Algorithm in Exponential Family | stat.ML | The Contrastive Divergence (CD) algorithm has achieved notable success in
training energy-based models including Restricted Boltzmann Machines and played
a key role in the emergence of deep learning. The idea of this algorithm is to
approximate the intractable term in the exact gradient of the log-likelihood
function b... | computer science |
22,360 | The Multiscale Laplacian Graph Kernel | stat.ML | Many real world graphs, such as the graphs of molecules, exhibit structure at
multiple different scales, but most existing kernels between graphs are either
purely local or purely global in character. In contrast, by building a
hierarchy of nested subgraphs, the Multiscale Laplacian Graph kernels (MLG
kernels) that we ... | computer science |
22,361 | Composing graphical models with neural networks for structured
representations and fast inference | stat.ML | We propose a general modeling and inference framework that composes
probabilistic graphical models with deep learning methods and combines their
respective strengths. Our model family augments graphical structure in latent
variables with neural network observation models. For inference, we extend
variational autoencode... | computer science |
22,362 | Data Augmentation via Levy Processes | stat.ML | If a document is about travel, we may expect that short snippets of the
document should also be about travel. We introduce a general framework for
incorporating these types of invariances into a discriminative classifier. The
framework imagines data as being drawn from a slice of a Levy process. If we
slice the Levy pr... | computer science |
22,363 | New metrics for learning and inference on sets, ontologies, and
functions | stat.ML | We propose new metrics on sets, ontologies, and functions that can be used in
various stages of probabilistic modeling, including exploratory data analysis,
learning, inference, and result interpretation. These new functions unify and
generalize some of the popular metrics on sets and functions, such as the
Jaccard and... | computer science |
22,364 | Predicting litigation likelihood and time to litigation for patents | stat.ML | Patent lawsuits are costly and time-consuming. An ability to forecast a
patent litigation and time to litigation allows companies to better allocate
budget and time in managing their patent portfolios. We develop predictive
models for estimating the likelihood of litigation for patents and the expected
time to litigati... | computer science |
22,365 | Evaluating the Performance of Offensive Linemen in the NFL | stat.ML | How does one objectively measure the performance of an individual offensive
lineman in the NFL? The existing literature proposes various measures that rely
on subjective assessments of game film, but has yet to develop an objective
methodology to evaluate performance. Using a variety of statistics related to
an offensi... | computer science |
22,366 | Clustering Time-Series Energy Data from Smart Meters | stat.ML | Investigations have been performed into using clustering methods in data
mining time-series data from smart meters. The problem is to identify patterns
and trends in energy usage profiles of commercial and industrial customers over
24-hour periods, and group similar profiles. We tested our method on energy
usage data p... | computer science |
22,367 | Skill-Based Differences in Spatio-Temporal Team Behavior in Defence of
The Ancients 2 | stat.ML | Multiplayer Online Battle Arena (MOBA) games are among the most played
digital games in the world. In these games, teams of players fight against each
other in arena environments, and the gameplay is focused on tactical combat.
Mastering MOBAs requires extensive practice, as is exemplified in the popular
MOBA Defence o... | computer science |
22,368 | Exact Bayesian inference for off-line change-point detection in
tree-structured graphical models | stat.ML | We consider the problem of change-point detection in multivariate
time-series. The multivariate distribution of the observations is supposed to
follow a graphical model, whose graph and parameters are affected by abrupt
changes throughout time. We demonstrate that it is possible to perform exact
Bayesian inference when... | computer science |
22,369 | Unified View of Matrix Completion under General Structural Constraints | stat.ML | In this paper, we present a unified analysis of matrix completion under
general low-dimensional structural constraints induced by {\em any} norm
regularization. We consider two estimators for the general problem of
structured matrix completion, and provide unified upper bounds on the sample
complexity and the estimatio... | computer science |
22,370 | A latent-observed dissimilarity measure | stat.ML | Quantitatively assessing relationships between latent variables and observed
variables is important for understanding and developing generative models and
representation learning. In this paper, we propose latent-observed
dissimilarity (LOD) to evaluate the dissimilarity between the probabilistic
characteristics of lat... | computer science |
22,371 | Sparse Representation of Multivariate Extremes with Applications to
Anomaly Ranking | stat.ML | Extremes play a special role in Anomaly Detection. Beyond inference and
simulation purposes, probabilistic tools borrowed from Extreme Value Theory
(EVT), such as the angular measure, can also be used to design novel
statistical learning methods for Anomaly Detection/ranking. This paper proposes
a new algorithm based o... | computer science |
22,372 | Directional Statistics in Machine Learning: a Brief Review | stat.ML | The modern data analyst must cope with data encoded in various forms,
vectors, matrices, strings, graphs, or more. Consequently, statistical and
machine learning models tailored to different data encodings are important. We
focus on data encoded as normalized vectors, so that their "direction" is more
important than th... | computer science |
22,373 | Contrastive Structured Anomaly Detection for Gaussian Graphical Models | stat.ML | Gaussian graphical models (GGMs) are probabilistic tools of choice for
analyzing conditional dependencies between variables in complex systems.
Finding changepoints in the structural evolution of a GGM is therefore
essential to detecting anomalies in the underlying system modeled by the GGM.
In order to detect structur... | computer science |
22,374 | Highly Accurate Prediction of Jobs Runtime Classes | stat.ML | Separating the short jobs from the long is a known technique to improve
scheduling performance. In this paper we describe a method we developed for
accurately predicting the runtimes classes of the jobs to enable this
separation. Our method uses the fact that the runtimes can be represented as a
mixture of overlapping ... | computer science |
22,375 | Fuzzy clustering of distribution-valued data using adaptive L2
Wasserstein distances | stat.ML | Distributional (or distribution-valued) data are a new type of data arising
from several sources and are considered as realizations of distributional
variables. A new set of fuzzy c-means algorithms for data described by
distributional variables is proposed.
The algorithms use the $L2$ Wasserstein distance between di... | computer science |
22,376 | Observational-Interventional Priors for Dose-Response Learning | stat.ML | Controlled interventions provide the most direct source of information for
learning causal effects. In particular, a dose-response curve can be learned by
varying the treatment level and observing the corresponding outcomes. However,
interventions can be expensive and time-consuming. Observational data, where
the treat... | computer science |
22,377 | Clustering on the Edge: Learning Structure in Graphs | stat.ML | With the recent popularity of graphical clustering methods, there has been an
increased focus on the information between samples. We show how learning
cluster structure using edge features naturally and simultaneously determines
the most likely number of clusters and addresses data scale issues. These
results are parti... | computer science |
22,378 | Matching models across abstraction levels with Gaussian Processes | stat.ML | Biological systems are often modelled at different levels of abstraction
depending on the particular aims/resources of a study. Such different models
often provide qualitatively concordant predictions over specific
parametrisations, but it is generally unclear whether model predictions are
quantitatively in agreement, ... | computer science |
22,379 | Mean Absolute Percentage Error for regression models | stat.ML | We study in this paper the consequences of using the Mean Absolute Percentage
Error (MAPE) as a measure of quality for regression models. We prove the
existence of an optimal MAPE model and we show the universal consistency of
Empirical Risk Minimization based on the MAPE. We also show that finding the
best model under... | computer science |
22,380 | Why (and How) Avoid Orthogonal Procrustes in Regularized Multivariate
Analysis | stat.ML | Multivariate Analysis (MVA) comprises a family of well-known methods for
feature extraction that exploit correlations among input variables of the data
representation. One important property that is enjoyed by most such methods is
uncorrelation among the extracted features. Recently, regularized versions of
MVA methods... | computer science |
22,381 | Destination Prediction by Trajectory Distribution Based Model | stat.ML | In this paper we propose a new method to predict the final destination of
vehicle trips based on their initial partial trajectories. We first review how
we obtained clustering of trajectories that describes user behaviour. Then, we
explain how we model main traffic flow patterns by a mixture of 2d Gaussian
distribution... | computer science |
22,382 | A note on the statistical view of matrix completion | stat.ML | A very simple interpretation of matrix completion problem is introduced based
on statistical models. Combined with the well-known results from missing data
analysis, such interpretation indicates that matrix completion is still a valid
and principled estimation procedure even without the missing completely at
random (M... | computer science |
22,383 | Kernel-Based Structural Equation Models for Topology Identification of
Directed Networks | stat.ML | Structural equation models (SEMs) have been widely adopted for inference of
causal interactions in complex networks. Recent examples include unveiling
topologies of hidden causal networks over which processes such as spreading
diseases, or rumors propagate. The appeal of SEMs in these settings stems from
their simplici... | computer science |
22,384 | Generalized Sparse Precision Matrix Selection for Fitting Multivariate
Gaussian Random Fields to Large Data Sets | stat.ML | We present a new method for estimating multivariate, second-order stationary
Gaussian Random Field (GRF) models based on the Sparse Precision matrix
Selection (SPS) algorithm, proposed by Davanloo et al. (2015) for estimating
scalar GRF models. Theoretical convergence rates for the estimated
between-response covariance... | computer science |
22,385 | ABtree: An Algorithm for Subgroup-Based Treatment Assignment | stat.ML | Given two possible treatments, there may exist subgroups who benefit greater
from one treatment than the other. This problem is relevant to the field of
marketing, where treatments may correspond to different ways of selling a
product. It is similarly relevant to the field of public policy, where
treatments may corresp... | computer science |
22,386 | Proceedings of the 5th Workshop on Machine Learning and Interpretation
in Neuroimaging (MLINI) at NIPS 2015 | stat.ML | This volume is a collection of contributions from the 5th Workshop on Machine
Learning and Interpretation in Neuroimaging (MLINI) at the Neural Information
Processing Systems (NIPS 2015) conference. Modern multivariate statistical
methods developed in the rapidly growing field of machine learning are being
increasingly... | computer science |
22,387 | Probing the Geometry of Data with Diffusion Fréchet Functions | stat.ML | Many complex ecosystems, such as those formed by multiple microbial taxa,
involve intricate interactions amongst various sub-communities. The most basic
relationships are frequently modeled as co-occurrence networks in which the
nodes represent the various players in the community and the weighted edges
encode levels o... | computer science |
22,388 | Orthogonal symmetric non-negative matrix factorization under the
stochastic block model | stat.ML | We present a method based on the orthogonal symmetric non-negative matrix
tri-factorization of the normalized Laplacian matrix for community detection in
complex networks. While the exact factorization of a given order may not exist
and is NP hard to compute, we obtain an approximate factorization by solving an
optimiz... | computer science |
22,389 | Online Algorithms For Parameter Mean And Variance Estimation In Dynamic
Regression Models | stat.ML | We study the problem of estimating the parameters of a regression model from
a set of observations, each consisting of a response and a predictor. The
response is assumed to be related to the predictor via a regression model of
unknown parameters. Often, in such models the parameters to be estimated are
assumed to be c... | computer science |
22,390 | False Discovery Rate Control and Statistical Quality Assessment of
Annotators in Crowdsourced Ranking | stat.ML | With the rapid growth of crowdsourcing platforms it has become easy and
relatively inexpensive to collect a dataset labeled by multiple annotators in a
short time. However due to the lack of control over the quality of the
annotators, some abnormal annotators may be affected by position bias which can
potentially degra... | computer science |
22,391 | Bayesian Variable Selection for Globally Sparse Probabilistic PCA | stat.ML | Sparse versions of principal component analysis (PCA) have imposed themselves
as simple, yet powerful ways of selecting relevant features of high-dimensional
data in an unsupervised manner. However, when several sparse principal
components are computed, the interpretation of the selected variables is
difficult since ea... | computer science |
22,392 | Stick-Breaking Variational Autoencoders | stat.ML | We extend Stochastic Gradient Variational Bayes to perform posterior
inference for the weights of Stick-Breaking processes. This development allows
us to define a Stick-Breaking Variational Autoencoder (SB-VAE), a Bayesian
nonparametric version of the variational autoencoder that has a latent
representation with stocha... | computer science |
22,393 | Learning to Discover Sparse Graphical Models | stat.ML | We consider structure discovery of undirected graphical models from
observational data. Inferring likely structures from few examples is a complex
task often requiring the formulation of priors and sophisticated inference
procedures. Popular methods rely on estimating a penalized maximum likelihood
of the precision mat... | computer science |
22,394 | Convergence guarantees for kernel-based quadrature rules in misspecified
settings | stat.ML | Kernel-based quadrature rules are becoming important in machine learning and
statistics, as they achieve super-$\sqrt{n}$ convergence rates in numerical
integration, and thus provide alternatives to Monte Carlo integration in
challenging settings where integrands are expensive to evaluate or where
integrands are high d... | computer science |
22,395 | Relevant sparse codes with variational information bottleneck | stat.ML | In many applications, it is desirable to extract only the relevant aspects of
data. A principled way to do this is the information bottleneck (IB) method,
where one seeks a code that maximizes information about a 'relevance' variable,
Y, while constraining the information encoded about the original data, X.
Unfortunate... | computer science |
22,396 | Local Minimax Complexity of Stochastic Convex Optimization | stat.ML | We extend the traditional worst-case, minimax analysis of stochastic convex
optimization by introducing a localized form of minimax complexity for
individual functions. Our main result gives function-specific lower and upper
bounds on the number of stochastic subgradient evaluations needed to optimize
either the functi... | computer science |
22,397 | Simultaneous Sparse Dictionary Learning and Pruning | stat.ML | Dictionary learning is a cutting-edge area in imaging processing, that has
recently led to state-of-the-art results in many signal processing tasks. The
idea is to conduct a linear decomposition of a signal using a few atoms of a
learned and usually over-completed dictionary instead of a pre-defined basis.
Determining ... | computer science |
22,398 | How priors of initial hyperparameters affect Gaussian process regression
models | stat.ML | The hyperparameters in Gaussian process regression (GPR) model with a
specified kernel are often estimated from the data via the maximum marginal
likelihood. Due to the non-convexity of marginal likelihood with respect to the
hyperparameters, the optimization may not converge to the global maxima. A
common approach to ... | computer science |
22,399 | A General Family of Trimmed Estimators for Robust High-dimensional Data
Analysis | stat.ML | We consider the problem of robustifying high-dimensional structured
estimation. Robust techniques are key in real-world applications which often
involve outliers and data corruption. We focus on trimmed versions of
structurally regularized M-estimators in the high-dimensional setting,
including the popular Least Trimme... | computer science |
22,400 | Predictive Coarse-Graining | stat.ML | We propose a data-driven, coarse-graining formulation in the context of
equilibrium statistical mechanics. In contrast to existing techniques which are
based on a fine-to-coarse map, we adopt the opposite strategy by prescribing a
probabilistic coarse-to-fine map. This corresponds to a directed probabilistic
model wher... | computer science |
22,401 | Scalable and Flexible Multiview MAX-VAR Canonical Correlation Analysis | stat.ML | Generalized canonical correlation analysis (GCCA) aims at finding latent
low-dimensional common structure from multiple views (feature vectors in
different domains) of the same entities. Unlike principal component analysis
(PCA) that handles a single view, (G)CCA is able to integrate information from
different feature ... | computer science |
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