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22,902 | Fair Kernel Learning | stat.ML | New social and economic activities massively exploit big data and machine
learning algorithms to do inference on people's lives. Applications include
automatic curricula evaluation, wage determination, and risk assessment for
credits and loans. Recently, many governments and institutions have raised
concerns about the ... | computer science |
22,903 | Time Series Prediction : Predicting Stock Price | stat.ML | Time series forecasting is widely used in a multitude of domains. In this
paper, we present four models to predict the stock price using the SPX index as
input time series data. The martingale and ordinary linear models require the
strongest assumption in stationarity which we use as baseline models. The
generalized li... | computer science |
22,904 | Sparse Linear Isotonic Models | stat.ML | In machine learning and data mining, linear models have been widely used to
model the response as parametric linear functions of the predictors. To relax
such stringent assumptions made by parametric linear models, additive models
consider the response to be a summation of unknown transformations applied on
the predict... | computer science |
22,905 | Good Arm Identification via Bandit Feedback | stat.ML | We consider a novel stochastic multi-armed bandit problem called {\em good
arm identification} (GAI), where a good arm is defined as an arm with expected
reward greater than or equal to a given threshold. GAI is a pure-exploration
problem that a single agent repeats a process of outputting an arm as soon as
it is ident... | computer science |
22,906 | Variational Inference based on Robust Divergences | stat.ML | Robustness to outliers is a central issue in real-world machine learning
applications. While replacing a model to a heavy-tailed one (e.g., from
Gaussian to Student-t) is a standard approach for robustification, it can only
be applied to simple models. In this paper, based on Zellner's optimization and
variational form... | computer science |
22,907 | Weighted Tensor Decomposition for Learning Latent Variables with Partial
Data | stat.ML | Tensor decomposition methods are popular tools for learning latent variables
given only lower-order moments of the data. However, the standard assumption is
that we have sufficient data to estimate these moments to high accuracy. In
this work, we consider the case in which certain dimensions of the data are not
always ... | computer science |
22,908 | Minimax Estimation of Bandable Precision Matrices | stat.ML | The inverse covariance matrix provides considerable insight for understanding
statistical models in the multivariate setting. In particular, when the
distribution over variables is assumed to be multivariate normal, the sparsity
pattern in the inverse covariance matrix, commonly referred to as the precision
matrix, cor... | computer science |
22,909 | Elliptical modeling and pattern analysis for perturbation models and
classfication | stat.ML | The characteristics (or numerical patterns) of a feature vector in the
transform domain of a perturbation model differ significantly from those of its
corresponding feature vector in the input domain. These differences - caused by
the perturbation techniques used for the transformation of feature patterns -
degrade the... | computer science |
22,910 | An Approach to One-Bit Compressed Sensing Based on Probably
Approximately Correct Learning Theory | stat.ML | In this paper, the problem of one-bit compressed sensing (OBCS) is formulated
as a problem in probably approximately correct (PAC) learning. It is shown that
the Vapnik-Chervonenkis (VC-) dimension of the set of half-spaces in
$\mathbb{R}^n$ generated by $k$-sparse vectors is bounded below by $k \lg
(n/k)$ and above by... | computer science |
22,911 | Fast MCMC sampling algorithms on polytopes | stat.ML | We propose and analyze two new MCMC sampling algorithms, the Vaidya walk and
the John walk, for generating samples from the uniform distribution over a
polytope. Both random walks are sampling algorithms derived from interior point
methods. The former is based on volumetric-logarithmic barrier introduced by
Vaidya wher... | computer science |
22,912 | Estimating the Operating Characteristics of Ensemble Methods | stat.ML | In this paper we present a technique for using the bootstrap to estimate the
operating characteristics and their variability for certain types of ensemble
methods. Bootstrapping a model can require a huge amount of work if the
training data set is large. Fortunately in many cases the technique lets us
determine the eff... | computer science |
22,913 | General Bayesian Inference over the Stiefel Manifold via the Givens
Transform | stat.ML | We introduce the Givens Transform, a novel transform between the space of
orthonormal matrices and $\mathbb{R}^D$. The Givens Transform allows for the
application of any general Bayesian inference algorithm to probabilistic models
containing constrained unit-vectors or orthonormal matrix parameters. This
includes a var... | computer science |
22,914 | Reparameterizing the Birkhoff Polytope for Variational Permutation
Inference | stat.ML | Many matching, tracking, sorting, and ranking problems require probabilistic
reasoning about possible permutations, a set that grows factorially with
dimension. Combinatorial optimization algorithms may enable efficient point
estimation, but fully Bayesian inference poses a severe challenge in this
high-dimensional, di... | computer science |
22,915 | From Distance Correlation to Multiscale Generalized Correlation | stat.ML | Understanding and developing a correlation measure that can detect general
dependencies is not only imperative to statistics and machine learning, but
also crucial to general scientific discovery in the big data age. We proposed
the Multiscale Generalized Correlation (MGC) in Shen et al. 2017 as a novel
correlation mea... | computer science |
22,916 | On denoising modulo 1 samples of a function | stat.ML | Consider an unknown smooth function $f: [0,1] \rightarrow \mathbb{R}$, and
say we are given $n$ noisy mod 1 samples of $f$, i.e., $y_i = (f(x_i) +
\eta_i)\mod 1$ for $x_i \in [0,1]$, where $\eta_i$ denotes noise. Given the
samples $(x_i,y_i)_{i=1}^{n}$, our goal is to recover smooth, robust estimates
of the clean sampl... | computer science |
22,917 | Probability Series Expansion Classifier that is Interpretable by Design | stat.ML | This work presents a new classifier that is specifically designed to be fully
interpretable. This technique determines the probability of a class outcome,
based directly on probability assignments measured from the training data. The
accuracy of the predicted probability can be improved by measuring more
probability es... | computer science |
22,918 | Globally Optimal Symbolic Regression | stat.ML | In this study we introduce a new technique for symbolic regression that
guarantees global optimality. This is achieved by formulating a mixed integer
non-linear program (MINLP) whose solution is a symbolic mathematical expression
of minimum complexity that explains the observations. We demonstrate our
approach by redis... | computer science |
22,919 | Distance-based classifier by data transformation for high-dimension,
strongly spiked eigenvalue models | stat.ML | We consider classifiers for high-dimensional data under the strongly spiked
eigenvalue (SSE) model. We first show that high-dimensional data often have the
SSE model. We consider a distance-based classifier using eigenstructures for
the SSE model. We apply the noise reduction methodology to estimation of the
eigenvalue... | computer science |
22,920 | Optimal Kernel-Based Dynamic Mode Decomposition | stat.ML | The state-of-the-art algorithm known as kernel-based dynamic mode
decomposition (K-DMD) provides a sub-optimal solution to the problem of reduced
modeling of a dynamical system based on a finite approximation of the Koopman
operator. It relies on crude approximations and on restrictive assumptions. The
purpose of this ... | computer science |
22,921 | Implicit Manifold Learning on Generative Adversarial Networks | stat.ML | This paper raises an implicit manifold learning perspective in Generative
Adversarial Networks (GANs), by studying how the support of the learned
distribution, modelled as a submanifold $\mathcal{M}_{\theta}$, perfectly match
with $\mathcal{M}_{r}$, the support of the real data distribution. We show that
optimizing Jen... | computer science |
22,922 | Latent Space Oddity: on the Curvature of Deep Generative Models | stat.ML | Deep generative models provide a systematic way to learn nonlinear data
distributions, through a set of latent variables and a nonlinear "generator"
function that maps latent points into the input space. The nonlinearity of the
generator imply that the latent space gives a distorted view of the input
space. Under mild ... | computer science |
22,923 | Synth-Validation: Selecting the Best Causal Inference Method for a Given
Dataset | stat.ML | Many decisions in healthcare, business, and other policy domains are made
without the support of rigorous evidence due to the cost and complexity of
performing randomized experiments. Using observational data to answer causal
questions is risky: subjects who receive different treatments also differ in
other ways that a... | computer science |
22,924 | A Large Dimensional Study of Regularized Discriminant Analysis
Classifiers | stat.ML | This article carries out a large dimensional analysis of standard regularized
discriminant analysis classifiers designed on the assumption that data arise
from a Gaussian mixture model with different means and covariances. The
analysis relies on fundamental results from random matrix theory (RMT) when
both the number o... | computer science |
22,925 | Fast Information-theoretic Bayesian Optimisation | stat.ML | Information-theoretic Bayesian optimisation techniques have demonstrated
state-of-the-art performance in tackling important global optimisation
problems. However, current information-theoretic approaches require many
approximations in implementation, introduce often-prohibitive computational
overhead and limit the choi... | computer science |
22,926 | Deep Recurrent Gaussian Process with Variational Sparse Spectrum
Approximation | stat.ML | Modeling sequential data has become more and more important in practice. Some
applications are autonomous driving, virtual sensors and weather forecasting.
To model such systems so called recurrent models are used. In this article we
introduce two new Deep Recurrent Gaussian Process (DRGP) models based on the
Sparse Sp... | computer science |
22,927 | Correcting Nuisance Variation using Wasserstein Distance | stat.ML | Profiling cellular phenotypes from microscopic imaging can provide meaningful
biological information resulting from various factors affecting the cells. One
motivating application is drug development: morphological cell features can be
captured from images, from which similarities between different drugs applied
at dif... | computer science |
22,928 | Independently Interpretable Lasso: A New Regularizer for Sparse
Regression with Uncorrelated Variables | stat.ML | Sparse regularization such as $\ell_1$ regularization is a quite powerful and
widely used strategy for high dimensional learning problems. The effectiveness
of sparse regularization has been supported practically and theoretically by
several studies. However, one of the biggest issues in sparse regularization is
that i... | computer science |
22,929 | Extracting low-dimensional dynamics from multiple large-scale neural
population recordings by learning to predict correlations | stat.ML | A powerful approach for understanding neural population dynamics is to
extract low-dimensional trajectories from population recordings using
dimensionality reduction methods. Current approaches for dimensionality
reduction on neural data are limited to single population recordings, and can
not identify dynamics embedde... | computer science |
22,930 | Flexible statistical inference for mechanistic models of neural dynamics | stat.ML | Mechanistic models of single-neuron dynamics have been extensively studied in
computational neuroscience. However, identifying which models can
quantitatively reproduce empirically measured data has been challenging. We
propose to overcome this limitation by using likelihood-free inference
approaches (also known as App... | computer science |
22,931 | Unsupervised Transformation Learning via Convex Relaxations | stat.ML | Our goal is to extract meaningful transformations from raw images, such as
varying the thickness of lines in handwriting or the lighting in a portrait. We
propose an unsupervised approach to learn such transformations by attempting to
reconstruct an image from a linear combination of transformations of its
nearest neig... | computer science |
22,932 | Large-Scale Optimal Transport and Mapping Estimation | stat.ML | This paper presents a novel two-step approach for the fundamental problem of
learning an optimal map from one distribution to another. First, we learn an
optimal transport (OT) plan, which can be thought as a one-to-many map between
the two distributions. To that end, we propose a stochastic dual approach of
regularize... | computer science |
22,933 | Bayesian model and dimension reduction for uncertainty propagation:
applications in random media | stat.ML | Well-established methods for the solution of stochastic partial differential
equations (SPDEs) typically struggle in problems with high-dimensional
inputs/outputs. Such difficulties are only amplified in large-scale
applications where even a few tens of full-order model runs are impractical.
While dimensionality reduct... | computer science |
22,934 | Universal consistency and minimax rates for online Mondrian Forests | stat.ML | We establish the consistency of an algorithm of Mondrian Forests, a
randomized classification algorithm that can be implemented online. First, we
amend the original Mondrian Forest algorithm, that considers a fixed lifetime
parameter. Indeed, the fact that this parameter is fixed hinders the
statistical consistency of ... | computer science |
22,935 | Variational Gaussian Dropout is not Bayesian | stat.ML | Gaussian multiplicative noise is commonly used as a stochastic regularisation
technique in training of deterministic neural networks. A recent paper
reinterpreted the technique as a specific algorithm for approximate inference
in Bayesian neural networks; several extensions ensued. We show that the
log-uniform prior us... | computer science |
22,936 | Can clustering scale sublinearly with its clusters? A variational EM
acceleration of GMMs and $k$-means | stat.ML | One iteration of $k$-means or EM for Gaussian mixture models (GMMs) scales
linearly with the number of data points $N$, the number of clusters $C$, and
the data dimensionality $D$. In this study, we explore whether one iteration of
$k$-means or EM for GMMs can scale sublinearly with $C$ at run-time, while the
increase ... | computer science |
22,937 | GPflowOpt: A Bayesian Optimization Library using TensorFlow | stat.ML | A novel Python framework for Bayesian optimization known as GPflowOpt is
introduced. The package is based on the popular GPflow library for Gaussian
processes, leveraging the benefits of TensorFlow including automatic
differentiation, parallelization and GPU computations for Bayesian
optimization. Design goals focus on... | computer science |
22,938 | Analyzing and Improving Stein Variational Gradient Descent for
High-dimensional Marginal Inference | stat.ML | Stein variational gradient descent (SVGD) is a nonparametric inference
method, which iteratively transports a set of randomly initialized particles to
approximate a differentiable target distribution, along the direction that
maximally decreases the KL divergence within a vector-valued reproducing kernel
Hilbert space ... | computer science |
22,939 | Model Criticism in Latent Space | stat.ML | Model criticism is usually carried out by assessing if replicated data
generated under the fitted model looks similar to the observed data, see e.g.
Gelman, Carlin, Stern, and Rubin (2004, p. 165). This paper presents a method
for latent variable models by pulling back the data into the space of latent
variables, and c... | computer science |
22,940 | Fast and reliable inference algorithm for hierarchical stochastic block
models | stat.ML | Network clustering reveals the organization of a network or corresponding
complex system with elements represented as vertices and interactions as edges
in a (directed, weighted) graph. Although the notion of clustering can be
somewhat loose, network clusters or groups are generally considered as nodes
with enriched in... | computer science |
22,941 | Kernel Conditional Exponential Family | stat.ML | A nonparametric family of conditional distributions is introduced, which
generalizes conditional exponential families using functional parameters in a
suitable RKHS. An algorithm is provided for learning the generalized natural
parameter, and consistency of the estimator is established in the well
specified case. In ex... | computer science |
22,942 | On consistent vertex nomination schemes | stat.ML | Given a vertex of interest in a network $G_1$, the vertex nomination problem
seeks to find the corresponding vertex of interest (if it exists) in a second
network $G_2$. Although the vertex nomination problem and related tasks have
attracted much attention in the machine learning literature, with applications
to social... | computer science |
22,943 | Spatial Mapping with Gaussian Processes and Nonstationary Fourier
Features | stat.ML | The use of covariance kernels is ubiquitous in the field of spatial
statistics. Kernels allow data to be mapped into high-dimensional feature
spaces and can thus extend simple linear additive methods to nonlinear methods
with higher order interactions. However, until recently, there has been a
strong reliance on a limi... | computer science |
22,944 | HodgeRank with Information Maximization for Crowdsourced Pairwise
Ranking Aggregation | stat.ML | Recently, crowdsourcing has emerged as an effective paradigm for
human-powered large scale problem solving in various domains. However, task
requester usually has a limited amount of budget, thus it is desirable to have
a policy to wisely allocate the budget to achieve better quality. In this
paper, we study the princi... | computer science |
22,945 | Improved Bayesian Compression | stat.ML | Compression of Neural Networks (NN) has become a highly studied topic in
recent years. The main reason for this is the demand for industrial scale usage
of NNs such as deploying them on mobile devices, storing them efficiently,
transmitting them via band-limited channels and most importantly doing
inference at scale. I... | computer science |
22,946 | A Double Parametric Bootstrap Test for Topic Models | stat.ML | Non-negative matrix factorization (NMF) is a technique for finding latent
representations of data. The method has been applied to corpora to construct
topic models. However, NMF has likelihood assumptions which are often violated
by real document corpora. We present a double parametric bootstrap test for
evaluating the... | computer science |
22,947 | Subgroup Identification and Interpretation with Bayesian Nonparametric
Models in Health Care Claims Data | stat.ML | Inpatient care is a large share of total health care spending, making
analysis of inpatient utilization patterns an important part of understanding
what drives health care spending growth. Common features of inpatient
utilization measures include zero inflation, over-dispersion, and skewness, all
of which complicate st... | computer science |
22,948 | Review on Parameter Estimation in HMRF | stat.ML | This is a technical report which explores the estimation methodologies on
hyper-parameters in Markov Random Field and Gaussian Hidden Markov Random
Field. In first section, we briefly investigate a theoretical framework on
Metropolis-Hastings algorithm. Next, by using MH algorithm, we simulate the
data from Ising model... | computer science |
22,949 | On the EM-Tau algorithm: a new EM-style algorithm with partial E-steps | stat.ML | The EM algorithm is one of many important tools in the field of statistics.
While often used for imputing missing data, its widespread applications include
other common statistical tasks, such as clustering. In clustering, the EM
algorithm assumes a parametric distribution for the clusters, whose parameters
are estimat... | computer science |
22,950 | Domain Generalization by Marginal Transfer Learning | stat.ML | Domain generalization is the problem of assigning class labels to an
unlabeled test data set, given several labeled training data sets drawn from
similar distributions. This problem arises in several applications where data
distributions fluctuate because of biological, technical, or other sources of
variation. We deve... | computer science |
22,951 | The Doctor Just Won't Accept That! | stat.ML | Calls to arms to build interpretable models express a well-founded discomfort
with machine learning. Should a software agent that does not even know what a
loan is decide who qualifies for one? Indeed, we ought to be cautious about
injecting machine learning (or anything else, for that matter) into
applications where t... | computer science |
22,952 | "I know it when I see it". Visualization and Intuitive Interpretability | stat.ML | Most research on the interpretability of machine learning systems focuses on
the development of a more rigorous notion of interpretability. I suggest that a
better understanding of the deficiencies of the intuitive notion of
interpretability is needed as well. I show that visualization enables but also
impedes intuitiv... | computer science |
22,953 | An Interpretable and Sparse Neural Network Model for Nonlinear Granger
Causality Discovery | stat.ML | While most classical approaches to Granger causality detection repose upon
linear time series assumptions, many interactions in neuroscience and economics
applications are nonlinear. We develop an approach to nonlinear Granger
causality detection using multilayer perceptrons where the input to the network
is the past t... | computer science |
22,954 | Variational Bayesian Inference For A Scale Mixture Of Normal
Distributions Handling Missing Data | stat.ML | In this paper, a scale mixture of Normal distributions model is developed for
classification and clustering of data having outliers and missing values. The
classification method, based on a mixture model, focuses on the introduction of
latent variables that gives us the possibility to handle sensitivity of model
to out... | computer science |
22,955 | An Efficient ADMM Algorithm for Structural Break Detection in
Multivariate Time Series | stat.ML | We present an efficient alternating direction method of multipliers (ADMM)
algorithm for segmenting a multivariate non-stationary time series with
structural breaks into stationary regions. We draw from recent work where the
series is assumed to follow a vector autoregressive model within segments and a
convex estimati... | computer science |
22,956 | Causal nearest neighbor rules for optimal treatment regimes | stat.ML | The estimation of optimal treatment regimes is of considerable interest to
precision medicine. In this work, we propose a causal $k$-nearest neighbor
method to estimate the optimal treatment regime. The method roots in the
framework of causal inference, and estimates the causal treatment effects
within the nearest neig... | computer science |
22,957 | No Classification without Representation: Assessing Geodiversity Issues
in Open Data Sets for the Developing World | stat.ML | Modern machine learning systems such as image classifiers rely heavily on
large scale data sets for training. Such data sets are costly to create, thus
in practice a small number of freely available, open source data sets are
widely used. We suggest that examining the geo-diversity of open data sets is
critical before ... | computer science |
22,958 | Predicting shim gaps in aircraft assembly with machine learning and
sparse sensing | stat.ML | A modern aircraft may require on the order of thousands of custom shims to
fill gaps between structural components in the airframe that arise due to
manufacturing tolerances adding up across large structures. These shims are
necessary to eliminate gaps, maintain structural performance, and minimize
pull-down forces req... | computer science |
22,959 | Causal Generative Neural Networks | stat.ML | We present Causal Generative Neural Networks (CGNNs) to learn functional
causal models from observational data. CGNNs leverage conditional
independencies and distributional asymmetries to discover bivariate and
multivariate causal structures. CGNNs make no assumption regarding the lack of
confounders, and learn a diffe... | computer science |
22,960 | Asymptotic Analysis via Stochastic Differential Equations of Gradient
Descent Algorithms in Statistical and Computational Paradigms | stat.ML | This paper investigates asymptotic behaviors of gradient descent algorithms
(particularly accelerated gradient descent and stochastic gradient descent) in
the context of stochastic optimization arose in statistics and machine learning
where objective functions are estimated from available data. We show that these
algor... | computer science |
22,961 | Proceedings of NIPS 2017 Workshop on Machine Learning for the Developing
World | stat.ML | This is the Proceedings of NIPS 2017 Workshop on Machine Learning for the
Developing World, held in Long Beach, California, USA on December 8, 2017 | computer science |
22,962 | Proceedings of NIPS 2017 Symposium on Interpretable Machine Learning | stat.ML | This is the Proceedings of NIPS 2017 Symposium on Interpretable Machine
Learning, held in Long Beach, California, USA on December 7, 2017 | computer science |
22,963 | Dependent relevance determination for smooth and structured sparse
regression | stat.ML | In many problem settings, parameter vectors are not merely sparse, but
dependent in such a way that non-zero coefficients tend to cluster together. We
refer to this form of dependency as "region sparsity". Classical sparse
regression methods, such as the lasso and automatic relevance determination
(ARD), which model pa... | computer science |
22,964 | Estimation and Optimization of Composite Outcomes | stat.ML | There is tremendous interest in precision medicine as a means to improve
patient outcomes by tailoring treatment to individual characteristics. An
individualized treatment rule formalizes precision medicine as a map from
patient information to a recommended treatment. A rule is defined to be optimal
if it maximizes the... | computer science |
22,965 | Predicting readmission risk from doctors' notes | stat.ML | We develop a model using deep learning techniques and natural language
processing on unstructured text from medical records to predict hospital-wide
$30$-day unplanned readmission, with c-statistic $.70$. Our model is
constructed to allow physicians to interpret the significant features for
prediction. | computer science |
22,966 | Faster ICA under orthogonal constraint | stat.ML | Independent Component Analysis (ICA) is a technique for unsupervised
exploration of multi-channel data widely used in observational sciences. In its
classical form, ICA relies on modeling the data as a linear mixture of
non-Gaussian independent sources. The problem can be seen as a likelihood
maximization problem. We i... | computer science |
22,967 | Particle Optimization in Stochastic Gradient MCMC | stat.ML | Stochastic gradient Markov chain Monte Carlo (SG-MCMC) has been increasingly
popular in Bayesian learning due to its ability to deal with large data. A
standard SG-MCMC algorithm simulates samples from a discretized-time Markov
chain to approximate a target distribution. However, the samples are typically
highly correl... | computer science |
22,968 | A Multi-Horizon Quantile Recurrent Forecaster | stat.ML | We propose a framework for general probabilistic multi-step time series
regression. Specifically, we exploit the expressiveness and temporal nature of
Recurrent Neural Networks, the nonparametric nature of Quantile Regression and
the efficiency of Direct Multi-Horizon Forecasting. A new training scheme for
recurrent ne... | computer science |
22,969 | Riemannian Stein Variational Gradient Descent for Bayesian Inference | stat.ML | We develop Riemannian Stein Variational Gradient Descent (RSVGD), a Bayesian
inference method that generalizes Stein Variational Gradient Descent (SVGD) to
Riemann manifold. The benefits are two-folds: (i) for inference tasks in
Euclidean spaces, RSVGD has the advantage over SVGD of utilizing information
geometry, and ... | computer science |
22,970 | TCAV: Relative concept importance testing with Linear Concept Activation
Vectors | stat.ML | Neural networks commonly offer high utility but remain difficult to
interpret. Developing methods to explain their decisions is challenging due to
their large size, complex structure, and inscrutable internal representations.
This work argues that the language of explanations should be expanded from that
of input featu... | computer science |
22,971 | Who wins the Miss Contest for Imputation Methods? Our Vote for Miss
BooPF | stat.ML | Missing data is an expected issue when large amounts of data is collected,
and several imputation techniques have been proposed to tackle this problem.
Beneath classical approaches such as MICE, the application of Machine Learning
techniques is tempting. Here, the recently proposed missForest imputation
method has show... | computer science |
22,972 | Thermostat-assisted Continuous-tempered Hamiltonian Monte Carlo for
Multimodal Posterior Sampling | stat.ML | In this paper, we propose a new sampling method named as the
thermostat-assisted continuous-tempered Hamiltonian Monte Carlo for multimodal
posterior sampling on large datasets. It simulates a noisy system, which is
augmented by a coupling tempering variable as well as a set of Nos\'e-Hoover
thermostats. This augmentat... | computer science |
22,973 | Prior and Likelihood Choices for Bayesian Matrix Factorisation on Small
Datasets | stat.ML | In this paper, we study the effects of different prior and likelihood choices
for Bayesian matrix factorisation, focusing on small datasets. These choices
can greatly influence the predictive performance of the methods. We identify
four groups of approaches: Gaussian-likelihood with real-valued priors,
nonnegative prio... | computer science |
22,974 | Intelligent EHRs: Predicting Procedure Codes From Diagnosis Codes | stat.ML | In order to submit a claim to insurance companies, a doctor needs to code a
patient encounter with both the diagnosis (ICDs) and procedures performed
(CPTs) in an Electronic Health Record (EHR). Identifying and applying relevant
procedures code is a cumbersome and time-consuming task as a doctor has to
choose from arou... | computer science |
22,975 | Bayesian Semi-nonnegative Tri-matrix Factorization to Identify Pathways
Associated with Cancer Types | stat.ML | Identifying altered pathways that are associated with specific cancer types
can potentially bring a significant impact on cancer patient treatment.
Accurate identification of such key altered pathways information can be used to
develop novel therapeutic agents as well as to understand the molecular
mechanisms of variou... | computer science |
22,976 | Survival-Supervised Topic Modeling with Anchor Words: Characterizing
Pancreatitis Outcomes | stat.ML | We introduce a new approach for topic modeling that is supervised by survival
analysis. Specifically, we build on recent work on unsupervised topic modeling
with so-called anchor words by providing supervision through an elastic-net
regularized Cox proportional hazards model. In short, an anchor word being
present in a... | computer science |
22,977 | Determinants of Mobile Money Adoption in Pakistan | stat.ML | In this work, we analyze the problem of adoption of mobile money in Pakistan
by using the call detail records of a major telecom company as our input. Our
results highlight the fact that different sections of the society have
different patterns of adoption of digital financial services but user mobility
related feature... | computer science |
22,978 | Exchangeable modelling of relational data: checking sparsity, train-test
splitting, and sparse exchangeable Poisson matrix factorization | stat.ML | A variety of machine learning tasks---e.g., matrix factorization, topic
modelling, and feature allocation---can be viewed as learning the parameters of
a probability distribution over bipartite graphs. Recently, a new class of
models for networks, the sparse exchangeable graphs, have been introduced to
resolve some imp... | computer science |
22,979 | High-dimensional robust regression and outliers detection with SLOPE | stat.ML | The problems of outliers detection and robust regression in a
high-dimensional setting are fundamental in statistics, and have numerous
applications. Following a recent set of works providing methods for
simultaneous robust regression and outliers detection, we consider in this
paper a model of linear regression with i... | computer science |
22,980 | Multiple Adaptive Bayesian Linear Regression for Scalable Bayesian
Optimization with Warm Start | stat.ML | Bayesian optimization (BO) is a model-based approach for gradient-free
black-box function optimization. Typically, BO is powered by a Gaussian process
(GP), whose algorithmic complexity is cubic in the number of evaluations.
Hence, GP-based BO cannot leverage large amounts of past or related function
evaluations, for e... | computer science |
22,981 | Fast Low-Rank Matrix Estimation without the Condition Number | stat.ML | In this paper, we study the general problem of optimizing a convex function
$F(L)$ over the set of $p \times p$ matrices, subject to rank constraints on
$L$. However, existing first-order methods for solving such problems either are
too slow to converge, or require multiple invocations of singular value
decompositions.... | computer science |
22,982 | Variational Inference over Non-differentiable Cardiac Simulators using
Bayesian Optimization | stat.ML | Performing inference over simulators is generally intractable as their
runtime means we cannot compute a marginal likelihood. We develop a
likelihood-free inference method to infer parameters for a cardiac simulator,
which replicates electrical flow through the heart to the body surface. We
improve the fit of a state-o... | computer science |
22,983 | Sensitivity Analysis for Predictive Uncertainty in Bayesian Neural
Networks | stat.ML | We derive a novel sensitivity analysis of input variables for predictive
epistemic and aleatoric uncertainty. We use Bayesian neural networks with
latent variables as a model class and illustrate the usefulness of our
sensitivity analysis on real-world datasets. Our method increases the
interpretability of complex blac... | computer science |
22,984 | The PhaseLift for Non-quadratic Gaussian Measurements | stat.ML | We study the problem of recovering a structured signal $\mathbf{x}_0$ from
high-dimensional measurements of the form $y=f(\mathbf{a}^T\mathbf{x}_0)$ for
some nonlinear function $f$. When the measurement vector $\mathbf a$ is iid
Gaussian, Brillinger observed in his 1982 paper that $\mu_\ell\cdot\mathbf{x}_0
= \min_{\ma... | computer science |
22,985 | A Mathematical Programming Approach for Integrated Multiple Linear
Regression Subset Selection and Validation | stat.ML | Subset selection for multiple linear regression aims to construct a
regression model that minimizes errors by selecting a small number of
explanatory variables. Once a model is built, various statistical tests and
diagnostics are conducted to validate the model and to determine whether
regression assumptions are met. M... | computer science |
22,986 | Path-Based Spectral Clustering: Guarantees, Robustness to Outliers, and
Fast Algorithms | stat.ML | We consider the problem of clustering with the longest leg path distance
(LLPD) metric, which is informative for elongated and irregularly shaped
clusters. We prove finite-sample guarantees on the performance of clustering
with respect to this metric when random samples are drawn from multiple
intrinsically low-dimensi... | computer science |
22,987 | Truncated Variational Sampling for "Black Box" Optimization of
Generative Models | stat.ML | We investigate the optimization of two probabilistic generative models with
binary latent variables using a novel variational EM approach. The approach
distinguishes itself from previous variational approaches by using latent
states as variational parameters. Here we use efficient and general purpose
sampling procedure... | computer science |
22,988 | Scalable Prototype Selection by Genetic Algorithms and Hashing | stat.ML | Classification in the dissimilarity space has become a very active research
area since it provides a possibility to learn from data given in the form of
pairwise non-metric dissimilarities, which otherwise would be difficult to cope
with. The selection of prototypes is a key step for the further creation of the
space. ... | computer science |
22,989 | A Composite Quantile Fourier Neural Network for Multi-Horizon
Probabilistic Forecasting | stat.ML | A novel quantile Fourier neural network is presented for nonparametric
probabilistic forecasting. Prediction are provided in the form of composite
quantiles using time as the only input to the model. This effectively is a form
of extrapolation based quantile regression applied for forecasting. Empirical
results showcas... | computer science |
22,990 | Orthogonal Machine Learning for Demand Estimation: High Dimensional
Causal Inference in Dynamic Panels | stat.ML | There has been growing interest in how economists can import machine learning
tools designed for prediction to accelerate and automate the model selection
process, while still retaining desirable inference properties for causal
parameters. Focusing on partially linear models, we extend the Double ML
framework to allow ... | computer science |
22,991 | Gauged Mini-Bucket Elimination for Approximate Inference | stat.ML | Computing the partition function $Z$ of a discrete graphical model is a
fundamental inference challenge. Since this is computationally intractable,
variational approximations are often used in practice. Recently, so-called
gauge transformations were used to improve variational lower bounds on $Z$. In
this paper, we pro... | computer science |
22,992 | Compressive sensing adaptation for polynomial chaos expansions | stat.ML | Basis adaptation in Homogeneous Chaos spaces rely on a suitable rotation of
the underlying Gaussian germ. Several rotations have been proposed in the
literature resulting in adaptations with different convergence properties. In
this paper we present a new adaptation mechanism that builds on compressive
sensing algorith... | computer science |
22,993 | Deep Gaussian Processes with Decoupled Inducing Inputs | stat.ML | Deep Gaussian Processes (DGP) are hierarchical generalizations of Gaussian
Processes (GP) that have proven to work effectively on a multiple supervised
regression tasks. They combine the well calibrated uncertainty estimates of GPs
with the great flexibility of multilayer models. In DGPs, given the inputs, the
outputs ... | computer science |
22,994 | Multivariate Bayesian Structural Time Series Model | stat.ML | This paper deals with inference and prediction for multiple correlated time
series, where one has also the choice of using a candidate pool of
contemporaneous predictors for each target series. Starting with a structural
model for the time-series, Bayesian tools are used for model fitting,
prediction, and feature selec... | computer science |
22,995 | Ranking Data with Continuous Labels through Oriented Recursive
Partitions | stat.ML | We formulate a supervised learning problem, referred to as continuous
ranking, where a continuous real-valued label Y is assigned to an observable
r.v. X taking its values in a feature space $\mathcal{X}$ and the goal is to
order all possible observations x in $\mathcal{X}$ by means of a scoring
function $s:\mathcal{X}... | computer science |
22,996 | Upgrading from Gaussian Processes to Student's-T Processes | stat.ML | Gaussian process priors are commonly used in aerospace design for performing
Bayesian optimization. Nonetheless, Gaussian processes suffer two significant
drawbacks: outliers are a priori assumed unlikely, and the posterior variance
conditioned on observed data depends only on the locations of those data, not
the assoc... | computer science |
22,997 | A graph-embedded deep feedforward network for disease outcome
classification and feature selection using gene expression data | stat.ML | Gene expression data represents a unique challenge in predictive model
building, because of the small number of samples $(n)$ compared to the huge
amount of features $(p)$. This "$n<<p$" property has hampered application of
deep learning techniques for disease outcome classification. Sparse learning by
incorporating ex... | computer science |
22,998 | Overpruning in Variational Bayesian Neural Networks | stat.ML | The motivations for using variational inference (VI) in neural networks
differ significantly from those in latent variable models. This has a
counter-intuitive consequence; more expressive variational approximations can
provide significantly worse predictions as compared to those with less
expressive families. In this ... | computer science |
22,999 | Nonparametric Hawkes Processes: Online Estimation and Generalization
Bounds | stat.ML | In this paper, we design a nonparametric online algorithm for estimating the
triggering functions of multivariate Hawkes processes. Unlike parametric
estimation, where evolutionary dynamics can be exploited for fast computation
of the gradient, and unlike typical function learning, where representer
theorem is readily ... | computer science |
23,000 | Information gain ratio correction: Improving prediction with more
balanced decision tree splits | stat.ML | Decision trees algorithms use a gain function to select the best split during
the tree's induction. This function is crucial to obtain trees with high
predictive accuracy. Some gain functions can suffer from a bias when it
compares splits of different arities. Quinlan proposed a gain ratio in C4.5's
information gain fu... | computer science |
23,001 | A Distributed Framework for the Construction of Transport Maps | stat.ML | The need to reason about uncertainty in large, complex, and multi-modal
datasets has become increasingly common across modern scientific environments.
The ability to transform samples from one distribution $P$ to another
distribution $Q$ enables the solution to many problems in machine learning
(e.g. Bayesian inference... | computer science |
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