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Rank-normalization, folding, and localization: An improved $\widehat{R}$ for assessing convergence of MCMCMar 19 2019Markov chain Monte Carlo is a key computational tool in Bayesian statistics, but it can be challenging to monitor the convergence of an iterative stochastic algorithm. In this paper we show that the convergence diagnostic $\widehat{R}$ of Gelman and Rubin ... More
Combining Model and Parameter Uncertainty in Bayesian Neural NetworksMar 18 2019Bayesian neural networks (BNNs) have recently regained a significant amount of attention in the deep learning community due to the development of scalable approximate Bayesian inference techniques. There are several advantages of using Bayesian approach: ... More
Variance reduction for MCMC methods via martingale representationsMar 18 2019In this paper we propose an efficient variance reduction approach for MCMC algorithms relying on a novel discrete time martingale representation for Markov chains. Our approach is fully non-asymptotic and does not require any type of ergodicity or special ... More
Fast Markov chain Monte Carlo for high dimensional Bayesian regression models with shrinkage priorsMar 16 2019In the past decade, many Bayesian shrinkage models have been developed for linear regression problems where the number of covariates, $p$, is large. Computing the intractable posterior are often done with three-block Gibbs samplers (3BG), based on representing ... More
High-dimensional nonparametric density estimation via symmetry and shape constraintsMar 14 2019We tackle the problem of high-dimensional nonparametric density estimation by taking the class of log-concave densities on $\mathbb{R}^p$ and incorporating within it symmetry assumptions, which facilitate scalable estimation algorithms and can mitigate ... More
colorspace: A Toolbox for Manipulating and Assessing Colors and PalettesMar 14 2019The R package colorspace provides a flexible toolbox for selecting individual colors or color palettes, manipulating these colors, and employing them in statistical graphics and data visualizations. In particular, the package provides a broad range of ... More
A Multi-armed Bandit MCMC, with applications in sampling from doubly intractable posteriorMar 13 2019Markov chain Monte Carlo (MCMC) algorithms are widely used to sample from complicated distributions, especially to sample from the posterior distribution in Bayesian inference. However, MCMC is not directly applicable when facing the doubly intractable ... More
HCmodelSets: An R package for specifying sets of well-fitting models in regression with a large number of potential explanatory variablesMar 13 2019In the context of regression with a large number of explanatory variables, Cox and Battey (2017) emphasize that if there are alternative reasonable explanations of the data that are statistically indistinguishable, one should aim to specify as many of ... More
Variational Estimators for Bayesian Optimal Experimental DesignMar 13 2019Bayesian optimal experimental design (BOED) is a principled framework for making efficient use of limited experimental resources. Unfortunately, its applicability is hampered by the difficulty of obtaining accurate estimates of the expected information ... More
A Generalized Correlated Random Walk Converging to Fractional Brownian MotionMar 13 2019We propose a new algorithm to generate a fractional Brownian motion, with a given Hurst parameter, 1/2<H<1 using the correlated Bernoulli random variables with parameter p; having a certain density. This density is constructed using the link between the ... More
A Generalized Correlated Random Walk Converging to Fractional Brownian MotionMar 13 2019Mar 15 2019We propose a new algorithm to generate a fractional Brownian motion, with a given Hurst parameter, 1/2<H<1 using the correlated Bernoulli random variables with parameter p; having a certain density. This density is constructed using the link between the ... More
A novel Bayesian approach for variable selection in linear regression modelsMar 13 2019We propose a novel Bayesian approach to the problem of variable selection in multiple linear regression models. In particular, we present a hierarchical setting which allows for direct specification of a-priori beliefs about the number of nonzero regression ... More
Generalized Elliptical Slice Sampling with Regional Pseudo-priorsMar 13 2019In this paper, we propose a MCMC algorithm based on elliptical slice sampling with the purpose to improve sampling efficiency. During sampling, a mixture distribution is fitted periodically to previous samples. The components of the mixture distribution ... More
ROC and AUC with a Binary Predictor: a Potentially Misleading MetricMar 12 2019In analysis of binary outcomes, the receiver operator characteristic (ROC) curve is heavily used to show the performance of a model or algorithm. The ROC curve is informative about the performance over a series of thresholds and can be summarized by the ... More
Elements of Sequential Monte CarloMar 12 2019A core problem in statistics and probabilistic machine learning is to compute probability distributions and expectations. This is the fundamental problem of Bayesian statistics and machine learning, which frames all inference as expectations with respect ... More
SmartEDA: An R Package for Automated Exploratory Data AnalysisMar 12 2019This paper introduces SmartEDA, which is an R package for performing Exploratory data analysis (EDA). EDA is generally the first step that one needs to perform before developing any machine learning or statistical models. The goal of EDA is to help someone ... More
Bayesian Allocation Model: Inference by Sequential Monte Carlo for Nonnegative Tensor Factorizations and Topic Models using Polya UrnsMar 11 2019We introduce a dynamic generative model, Bayesian allocation model (BAM), which establishes explicit connections between nonnegative tensor factorization (NTF), graphical models of discrete probability distributions and their Bayesian extensions, and ... More
NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural TransportMar 09 2019Hamiltonian Monte Carlo is a powerful algorithm for sampling from difficult-to-normalize posterior distributions. However, when the geometry of the posterior is unfavorable, it may take many expensive evaluations of the target distribution and its gradient ... More
Streamlined Computing for Variational Inference with Higher Level Random EffectsMar 07 2019We derive and present explicit algorithms to facilitate streamlined computing for variational inference for models containing higher level random effects. Existing literature, such as Lee and Wand (2016), is such that streamlined variational inference ... More
Bayesian spatially varying coefficient models in the spBayes R packageMar 07 2019This paper describes and illustrates the addition of the spSVC function to the spBayes R package. The spSVC function uses a computationally efficient Markov chain Monte Carlo algorithm detailed in FBG15 and extends current spBayes functions, that fit ... More
Estimation and uncertainty quantification for the output from quantum simulatorsMar 07 2019The problem of estimating certain distributions over $\{0,1\}^d$ is considered here. The distribution represents a quantum system of $d$ qubits, where there are non-trivial dependencies between the qubits. A maximum entropy approach is adopted to reconstruct ... More
GRATIS: GeneRAting TIme Series with diverse and controllable characteristicsMar 07 2019The explosion of time series data in recent years has brought a flourish of new time series analysis methods, for forecasting, clustering, classification and other tasks. The evaluation of these new methods requires a diverse collection of time series ... More
Simultaneous Prediction Intervals for Small Area ParameterMar 07 2019In this paper we address the construction of simultaneous prediction intervals for small area parameters in linear mixed models. Simultaneous intervals are necessary to compare areas, or to look at several areas at a time, as the presently available intervals ... More
Hamiltonian Monte Carlo on Symmetric and Homogeneous Spaces via Symplectic ReductionMar 07 2019The Hamiltonian Monte Carlo method generates samples by introducing a mechanical system that explores the target density. For distributions on manifolds it is not always simple to perform the mechanics as a result of the lack of global coordinates, the ... More
Causal Discovery Toolbox: Uncover causal relationships in PythonMar 06 2019This paper presents a new open source Python framework for causal discovery from observational data and domain background knowledge, aimed at causal graph and causal mechanism modeling. The 'cdt' package implements the end-to-end approach, recovering ... More
Bayesian inference and uncertainty quantification for image reconstruction with Poisson dataMar 05 2019We provide a complete framework for performing infinite-dimensional Bayesian inference and uncertainty quantification for image reconstruction with Poisson data. In particular, we address the following issues to make the Bayesian framework applicable ... More
Bayesian inference and uncertainty quantification for image reconstruction with Poisson dataMar 05 2019Mar 07 2019We provide a complete framework for performing infinite-dimensional Bayesian inference and uncertainty quantification for image reconstruction with Poisson data. In particular, we address the following issues to make the Bayesian framework applicable ... More
Convex Covariate Clustering for ClassificationMar 05 2019Clustering, like covariate selection for classification, is an important step to understand and interpret the data. However, clustering of covariates is often performed independently of the classification step, which can lead to undesirable clustering ... More
Quantifying Gait Changes Using Microsoft Kinect and Sample EntropyMar 05 2019This study describes a method to quantify potential gait changes in human subjects. Microsoft Kinect devices were used to provide and track coordinates of fifteen different joints of a subject over time. Three male subjects walk a 10-foot path multiple ... More
Spectral Density-Based and Measure-Preserving ABC for partially observed diffusion processes. An illustration on Hamiltonian SDEsMar 04 2019Approximate Bayesian Computation (ABC) has become one of the major tools of likelihood-free statistical inference in complex mathematical models. Simultaneously, stochastic differential equations (SDEs) have developed to an established tool for modelling ... More
Similarity-based Random Survival ForestMar 04 2019Predicting the time to a clinical outcome for patients in intensive care units (ICUs) helps to support critical medical treatment decisions. The time to an event of interest could be, for example, survival time or time to recovery from a disease/ailment ... More
Bernoulli Race Particle FiltersMar 03 2019When the weights in a particle filter are not available analytically, standard resampling methods cannot be employed. To circumvent this problem state-of-the-art algorithms replace the true weights with non-negative unbiased estimates. This algorithm ... More
Scalable optimization-based sampling on function spaceMar 03 2019Optimization-based samplers provide an efficient and parallellizable approach to solving large-scale Bayesian inverse problems. These methods solve randomly perturbed optimization problems to draw samples from an approximate posterior distribution. "Correcting" ... More
Construction Methods for GaussoidsFeb 28 2019The number of $n$-gaussoids is shown to be a double exponential function in $n$. The necessary bounds are achieved by studying construction methods for gaussoids that rely on prescribing $3$-minors and encoding the resulting combinatorial constraints ... More
Adaptive Gaussian Copula ABCFeb 27 2019Approximate Bayesian computation (ABC) is a set of techniques for Bayesian inference when the likelihood is intractable but sampling from the model is possible. This work presents a simple yet effective ABC algorithm based on the combination of two classical ... More
Efficient Bayesian inference for univariate and multivariate non linear state space models with univariate autoregressive state equationFeb 27 2019Latent autoregressive processes are a popular choice to model time varying parameters. These models can be formulated as non linear state space models for which inference is not straightforward due to the high number of parameters. Therefore maximum likelihood ... More
Efficient Bayesian inference for univariate and multivariate non linear state space models with univariate autoregressive state equationFeb 27 2019Feb 28 2019Latent autoregressive processes are a popular choice to model time varying parameters. These models can be formulated as non linear state space models for which inference is not straightforward due to the high number of parameters. Therefore maximum likelihood ... More
Gait Change Detection Using Parameters Generated from Microsoft Kinect CoordinatesFeb 27 2019This paper describes a method to convert Microsoft Kinect coordinates into gait parameters in order to detect a person's gait change. The proposed method can help quantify the progress of physical therapy. Microsoft Kinect, a popular platform for video ... More
Estimation of the Parameters of Multivariate Stable DistributionsFeb 26 2019In this paper, we begin our discussion with some of the well-known methods available in the literature for the estimation of the parameters of a univariate/multivariate stable distribution. Based on the available methods, a new hybrid method is proposed ... More
Estimating Atmospheric Motion Winds from Satellite Image Data using Space-time Drift ModelsFeb 25 2019Geostationary satellites collect high-resolution weather data comprising a series of images which can be used to estimate wind speed and direction at different altitudes. The Derived Motion Winds (DMW) Algorithm is commonly used to process these data ... More
Binscatter RegressionsFeb 25 2019We introduce the \texttt{Stata} (and \texttt{R}) package \textsf{Binsreg}, which implements the binscatter methods developed in \citet*{Cattaneo-Crump-Farrell-Feng_2019_Binscatter}. The package includes the commands \texttt{binsreg}, \texttt{binsregtest}, ... More
Acceleration of expensive computations in Bayesian statistics using vector operationsFeb 25 2019Many applications in Bayesian statistics are extremely computationally intensive. However, they are also often inherently parallel, making them prime targets for modern massively parallel central processing unit (CPU) architectures. While the use of multi-core ... More
Snowboot: Bootstrap Methods for Network InferenceFeb 24 2019Complex networks are used to describe a broad range of disparate social systems and natural phenomena, from power grids to customer segmentation to human brain connectome. Challenges of parametric model specification and validation inspire a search for ... More
Nonconvex sampling with the Metropolis-adjusted Langevin algorithmFeb 22 2019The Langevin Markov chain algorithms are widely deployed methods to sample from distributions in challenging high-dimensional and non-convex statistics and machine learning applications. Despite this, current bounds for the Langevin algorithms are slower ... More
BayesMallows: An R Package for the Bayesian Mallows ModelFeb 22 2019BayesMallows is an R package for analyzing data in the form of rankings or preferences with the Mallows rank model, and its finite mixture extension, in a Bayesian probabilistic framework. The Mallows model is a well-known model, grounded on the idea ... More
Online Sampling from Log-Concave DistributionsFeb 21 2019Given a sequence of convex functions $f_0, f_1, \ldots, f_T$, we study the problem of sampling from the Gibbs distribution $\pi_t \propto e^{-\sum_{k=0}^t f_k}$ for each epoch $t$ in an online manner. This problem occurs in applications to machine learning, ... More
Online Sampling from Log-Concave DistributionsFeb 21 2019Mar 08 2019Given a sequence of convex functions $f_0, f_1, \ldots, f_T$, we study the problem of sampling from the Gibbs distribution $\pi_t \propto e^{-\sum_{k=0}^t f_k}$ for each epoch $t$ in an online manner. This problem occurs in applications to machine learning, ... More
Malaria Incidence in the Philippines: Prediction using the Autoregressive Moving Average ModelsFeb 21 2019The study was conducted to develop an appropriate model that could predict the weekly reported Malaria incidence in the Philippines using the Box-Jenkins method.The data were retrieved from the Department of Health(DOH) website in the Philippines. It ... More
Cross Validation for Penalized Quantile Regression with a Case-Weight Adjusted Solution PathFeb 20 2019Cross validation is widely used for selecting tuning parameters in regularization methods, but it is computationally intensive in general. To lessen its computational burden, approximation schemes such as generalized approximate cross validation (GACV) ... More
EcoMem: An R package for quantifying ecological memoryFeb 20 2019Ecological processes may exhibit memory to past disturbances affecting the resilience of ecosystems to future disturbance. Understanding the role of ecological memory in shaping ecosystem responses to disturbance under global change is a critical step ... More
Computation of the expected value of a function of a chi-distributed random variableFeb 19 2019We consider the problem of numerically evaluating the expected value of a smooth bounded function of a chi-distributed random variable, divided by the square root of the number of degrees of freedom. This problem arises in the contexts of simultaneous ... More
Is a single unique Bayesian network enough to accurately represent your data?Feb 18 2019Bayesian network (BN) modelling is extensively used in systems epidemiology. Usually it consists in selecting and reporting the best-fitting structure conditional to the data. A major practical concern is avoiding overfitting, on account of its extreme ... More
Optimal Scaling and Shaping of Random Walk Metropolis via Diffusion Limits of Block-I.I.D. TargetsFeb 18 2019This work extends Roberts et al. (1997) by considering limits of Random Walk Metropolis (RWM) applied to block IID target distributions, with corresponding block-independent proposals. The extension verifies the robustness of the optimal scaling heuristic, ... More
The Arctic curve for Aztec rectangles with defects via the Tangent MethodFeb 18 2019The Tangent Method of Colomo and Sportiello is applied to the study of the asymptotics of domino tilings of large Aztec rectangles, with some fixed distribution of defects along a boundary. The associated Non-Intersecting Lattice Path configurations are ... More
LISA: a MATLAB package for Longitudinal Image Sequence AnalysisFeb 16 2019Large sequences of images (or movies) can now be obtained on an unprecedented scale, which poses fundamental challenges to the existing image analysis techniques. The challenges include heterogeneity, (automatic) alignment, multiple comparisons, potential ... More
Projected Pólya TreeFeb 16 2019One way of defining probability distributions for circular variables (directions in two dimensions) is to radially project probability distributions, originally defined on $\mathbb{R}^2$, to the unit circle. Projected distributions have proved to be useful ... More
A New Smoothing Technique based on the Parallel Concatenation of Forward/Backward Bayesian Filters: Turbo SmoothingFeb 15 2019Recently, a novel method for developing filtering algorithms, based on the parallel concatenation of Bayesian filters and called turbo filtering, has been proposed. In this manuscript we show how the same conceptual approach can be exploited to devise ... More
Generalized semimodularity: order statisticsFeb 14 2019A notion of generalized $n$-semimodularity is introduced, which extends that of (sub/super)mod\-ularity in four ways at once. The main result of this paper, stating that every generalized $(n\colon\!2)$-semimodular function on the $n$th Cartesian power ... More
A new estimator for Weibull distribution parameters: Comprehensive comparative study for Weibull DistributionFeb 14 2019Weibull distribution has received a wide range of applications in engineering and science. The utility and usefulness of an estimator is highly subject to the field of practitioner's study. In practice users looking for their desired estimator under different ... More
Bayesian inference and non-linear extensions of the CIRCE method for quantifying the uncertainty of closure relationships integrated into thermal-hydraulic system codesFeb 13 2019Uncertainty Quantification of closure relationships integrated into thermal-hydraulic system codes is a critical prerequisite so that the Best-Estimate Plus Uncertainty (BEPU) methodology for nuclear safety and licensing processes can be implemented. ... More
Bayesian inference using synthetic likelihood: asymptotics and adjustmentsFeb 13 2019Implementing Bayesian inference is often computationally challenging in applications involving complex models, and sometimes calculating the likelihood itself can be difficult. Synthetic likelihood is one approach for carrying out inference when the likelihood ... More
Derivative-based global sensitivity analysis for models with high-dimensional inputs and functional outputsFeb 12 2019We present a framework for derivative-based global sensitivity analysis (GSA) for models with high-dimensional input parameters and functional outputs. We combine ideas from derivative-based GSA, random field representation via Karhunen--Lo\`{e}ve expansions, ... More
Derivative-based global sensitivity analysis for models with high-dimensional inputs and functional outputsFeb 12 2019Feb 17 2019We present a framework for derivative-based global sensitivity analysis (GSA) for models with high-dimensional input parameters and functional outputs. We combine ideas from derivative-based GSA, random field representation via Karhunen--Lo\`{e}ve expansions, ... More
Optimal BIBD-extended designsFeb 12 2019Balanced incomplete block designs (BIBDs) are a class of designs with v treatments and b blocks of size k that are optimal with regards to a wide range of optimality criteria, but it is not clear which designs to choose for combinations of v, b and k ... More
Projected Data AssimilationFeb 12 2019We introduce a framework for Data Assimilation (DA) in which the data is split into multiple sets corresponding to low-rank projections of the state space. Algorithms are developed that assimilate some or all of the projected data, including an algorithm ... More
Computing Extremely Accurate Quantiles Using t-DigestsFeb 11 2019We present on-line algorithms for computing approximations of rank-based statistics that give high accuracy, particularly near the tails of a distribution, with very small sketches. Notably, the method allows a quantile $q$ to be computed with an accuracy ... More
KTBoost: Combined Kernel and Tree BoostingFeb 11 2019In this article, we introduce a novel boosting algorithm called `KTBoost', which combines kernel boosting and tree boosting. In each boosting iteration, the algorithm adds either a regression tree or reproducing kernel Hilbert space (RKHS) regression ... More
Space-efficient estimation of empirical tail dependence coefficients for bivariate data streamsFeb 10 2019This article provides an extension to recent work on the development of a space-efficient summary for bivariate empirical copula approximations in a streaming data regime. The extension proposed here considers the case when one would like to accurately ... More
Mini-batch learning of exponential family finite mixture modelsFeb 09 2019Mini-batch algorithms have become increasingly popular due to the requirement for solving optimization problems, based on large-scale data sets. Using an existing online expectation--maximization (EM) algorithm framework, we demonstrate how mini-batch ... More
Bayesian Model Selection with Graph Structured SparsityFeb 08 2019We propose a general algorithmic framework for Bayesian model selection. A spike-and-slab Laplacian prior is introduced to model the underlying structural assumption. Using the notion of effective resistance, we derive an EM-type algorithm with closed-form ... More
Estimation of variance components, heritability and the ridge penalty in high-dimensional generalized linear modelsFeb 07 2019For high-dimensional linear regression models, we review and compare several estimators of variances $\tau^2$ and $\sigma^2$ of the random slopes and errors, respectively. These variances relate directly to ridge regression penalty $\lambda$ and heritability ... More
The exact phase diagram for a semipermeable TASEP with nonlocal boundary jumpsFeb 06 2019We consider a finite one-dimensional totally asymmetric simple exclusion process (TASEP) with four types of particles, $\{1,0,\bar{1},*\}$, in contact with reservoirs. Particles of species $0$ can neither enter nor exit the lattice, and those of species ... More
CodedReduce: A Fast and Robust Framework for Gradient Aggregation in Distributed LearningFeb 06 2019We focus on the commonly used synchronous Gradient Descent paradigm for large-scale distributed learning, for which there has been a growing interest to develop efficient and robust gradient aggregation strategies that overcome two key bottlenecks: communication ... More
Unbiased Smoothing using Particle Independent Metropolis-HastingsFeb 05 2019We consider the approximation of expectations with respect to the distribution of a latent Markov process given noisy measurements. This is known as the smoothing problem and is often approached with particle and Markov chain Monte Carlo (MCMC) methods. ... More
Learning Hierarchical Interactions at Scale: A Convex Optimization ApproachFeb 05 2019Feb 06 2019In many learning settings, it is beneficial to augment the main features with pairwise interactions. Such interaction models can be often enhanced by performing variable selection under the so-called strong hierarchy constraint: an interaction is non-zero ... More
Global Fitting of the Response Surface via Estimating Multiple Contours of a SimulatorFeb 04 2019Computer simulators are nowadays widely used to understand complex physical systems in many areas such as aerospace, renewable energy, climate modeling, and manufacturing. One fundamental issue in the study of computer simulators is known as experimental ... More
Methods of interpreting error estimates for grayscale image reconstructionsFeb 02 2019One representation of possible errors in a grayscale image reconstruction is as another grayscale image estimating potentially worrisome differences between the reconstruction and the actual "ground-truth" reality. Visualizations and summary statistics ... More
On the use of ABC-MCMC with inflated tolerance and post-correctionFeb 01 2019Approximate Bayesian computation (ABC) allows for inference of complicated probabilistic models with intractable likelihoods using model simulations. The ABC Markov chain Monte Carlo (MCMC) inference is often sensitive to the tolerance parameter: low ... More
Trading beams for bandwidth: Imaging with randomized beamformingJan 31 2019We study the problem of actively imaging a range-limited far-field scene using an antenna array. We describe how the range limit imposes structure in the measurements across multiple wavelengths. This structure allows us to introduce a novel trade-off: ... More
Transport map accelerated adaptive importance sampling, and application to inverse problems arising from multiscale stochastic reaction networksJan 31 2019In many applications, Bayesian inverse problems can give rise to probability distributions which contain complexities due to the Hessian varying greatly across parameter space. This complexity often manifests itself as lower dimensional manifolds on which ... More
Exact Bootstrap and Permutation Distribution of Wins and Losses in a Hierarchical TrialJan 30 2019Finkelstein-Schoenfeld, Buyse, Pocock, and other authors have developed generalizations of the Mann-Whitney test that allow for pairwise patient comparisons to include a hierarchy of measurements. Various authors present either asymptotic or randomized ... More
Stochastic Gradient MCMC for Nonlinear State Space ModelsJan 29 2019State space models (SSMs) provide a flexible framework for modeling complex time series via a latent stochastic process. Inference for nonlinear, non-Gaussian SSMs is often tackled with particle methods that do not scale well to long time series. The ... More
Differentially Private Markov Chain Monte CarloJan 29 2019Recent developments in differentially private (DP) machine learning and DP Bayesian learning have enabled learning under strong privacy guarantees for the training data subjects. In this paper, we further extend the applicability of DP Bayesian learning ... More
A new tidy data structure to support exploration and modeling of temporal dataJan 29 2019Mining temporal data for information is often inhibited by a multitude of formats: irregular or multiple time intervals, point events that need aggregating, multiple observational units or repeated measurements on multiple individuals, and heterogeneous ... More
A new tidy data structure to support exploration and modeling of temporal dataJan 29 2019Feb 13 2019Mining temporal data for information is often inhibited by a multitude of formats: irregular or multiple time intervals, point events that need aggregating, multiple observational units or repeated measurements on multiple individuals, and heterogeneous ... More
Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian ComputationJan 29 2019We present a novel family of deep neural architectures, named partially exchangeable networks (PENs) that leverage probabilistic symmetries. By design, PENs are invariant to block-switch transformations, which characterize the partial exchangeability ... More
Variational Characterizations of Local Entropy and Heat Regularization in Deep LearningJan 29 2019The aim of this paper is to provide new theoretical and computational understanding on two loss regularizations employed in deep learning, known as local entropy and heat regularization. For both regularized losses we introduce variational characterizations ... More
Self-Assembly of Geometric Space from Random GraphsJan 28 2019We present a Euclidean quantum gravity model in which random graphs dynamically self-assemble into discrete manifold structures. Concretely, we consider a statistical model driven by a discretisation of the Euclidean Einstein-Hilbert action; contrary ... More
A dynamic stochastic blockmodel for interaction lengthsJan 28 2019We propose a new dynamic stochastic blockmodel that focuses on the analysis of interaction lengths in networks. The model does not rely on a discretization of the time dimension and may be used to analyze networks that evolve continuously over time. The ... More
Approximation of Wasserstein distance with TransshipmentJan 27 2019An algorithm for approximating the p-Wasserstein distance between histograms defined on unstructured discrete grids is presented. It is based on the computation of a barycenter constrained to be supported on a low dimensional subspace, which corresponds ... More
Approximation of Wasserstein distance with TransshipmentJan 27 2019Feb 12 2019An algorithm for approximating the p-Wasserstein distance between histograms defined on unstructured discrete grids is presented. It is based on the computation of a barycenter constrained to be supported on a low dimensional subspace, which corresponds ... More
Local dimension reduction of summary statistics for likelihood-free inferenceJan 25 2019Approximate Bayesian computation (ABC) and other likelihood-free inference methods have gained popularity in the last decade, as they allow rigorous statistical inference for complex models without analytically tractable likelihood functions. A key component ... More
Fast Markov Chain Monte Carlo Algorithms via Lie GroupsJan 24 2019From basic considerations of the Lie group that preserves a target probability measure, we derive the Barker, Metropolis, and ensemble Markov chain Monte Carlo (MCMC) algorithms, as well as two new MCMC algorithms. The convergence properties of these ... More
Visualizing Topographic Independent Component Analysis with MoviesJan 24 2019Independent component analysis (ICA) has often been used as a tool to model natural image statistics by separating multivariate signals in the image into components that are assumed to be independent. However, these estimated components oftentimes have ... More
Large dimensional analysis of general margin based classification methodsJan 23 2019Margin-based classifiers have been popular in both machine learning and statistics for classification problems. Since a large number of classifiers are available, one natural question is which type of classifiers should be used given a particular classification ... More
A Conway-Maxwell-Poisson GARMA Model for Count DataJan 22 2019We propose a flexible model for count time series which has potential uses for both underdispersed and overdispersed data. The model is based on the Conway-Maxwell-Poisson (COM-Poisson) distribution with parameters varying along time to take serial correlation ... More
A Fast Iterative Algorithm for High-dimensional Differential NetworkJan 22 2019Differential network is an important tool to capture the changes of conditional correlations under two sample cases. In this paper, we introduce a fast iterative algorithm to recover the differential network for high-dimensional data. The computation ... More
Spectral Graph Analysis: A Unified Explanation and Modern PerspectivesJan 21 2019Complex networks or graphs are ubiquitous in sciences and engineering: biological networks, brain networks, transportation networks, social networks, and the World Wide Web, to name a few. Spectral graph theory provides a set of useful techniques and ... More
A weighted Discrepancy Bound of quasi-Monte Carlo Importance SamplingJan 21 2019Importance sampling Monte-Carlo methods are widely used for the approximation of expectations with respect to partially known probability measures. In this paper we study a deterministic version of such an estimator based on quasi-Monte Carlo. We obtain ... More
Unreasonable effectiveness of Monte CarloJan 18 2019This is a comment on the article "Probabilistic Integration: A Role in Statistical Computation?" by F.-X. Briol, C. J. Oates, M. Girolami, M. A. Osborne and D. Sejdinovic to appear in Statistical Science. There is a role for statistical computation in ... More