
Generalized infinite factorization models
Factorization models express a statistical object of interest in terms o...
read it

Covariateinformed latent interaction models: Addressing geographic taxonomic bias in predicting birdplant interactions
Climate change and reductions in natural habitats necessitate that we be...
read it

PPA: Principal Parcellation Analysis for Brain Connectomes and Multiple Traits
Our understanding of the structure of the brain and its relationships wi...
read it

Closer than they appear: A Bayesian perspective on individuallevel heterogeneity in risk assessment
Risk assessment instruments are used across the criminal justice system ...
read it

Identifying Interpretable Discrete Latent Structures from Discrete Data
High dimensional categorical data are routinely collected in biomedical ...
read it

GridParametrizeSplit (GriPS) for Improved Scalable Inference in Spatial Big Data Analysis
Rapid advancements in spatial technologies including Geographic Informat...
read it

Spatial Multivariate Trees for Big Data Bayesian Regression
High resolution geospatial data are challenging because standard geostat...
read it

Accelerated Algorithms for Convex and NonConvex Optimization on Manifolds
We propose a general scheme for solving convex and nonconvex optimizati...
read it

Diffusion Based Gaussian Processes on Restricted Domains
In nonparametric regression and spatial process modeling, it is common f...
read it

Bayesian Matrix Completion for Hypothesis Testing
The United States Environmental Protection Agency (EPA) screens thousand...
read it

Density estimation and modeling on symmetric spaces
In many applications, data and/or parameters are supported on nonEuclid...
read it

Extended Stochastic Block Models
Stochastic block models (SBM) are widely used in network science due to ...
read it

Robust Optimization and Inference on Manifolds
We propose a robust and scalable procedure for general optimization and ...
read it

A generalized Bayes framework for probabilistic clustering
Lossbased clustering methods, such as kmeans and its variants, are sta...
read it

Bayesian clustering of highdimensional data
In many applications, it is of interest to cluster subjects based on ver...
read it

Classification Trees for Imbalanced and Sparse Data: SurfacetoVolume Regularization
Classification algorithms face difficulties when one or more classes hav...
read it

Bayesian semiparametric long memory models for discretized event data
We introduce a new class of semiparametric latent variable models for lo...
read it

Composite mixture of loglinear models for categorical data
Multivariate categorical data are routinely collected in many applicatio...
read it

Distributed Bayesian clustering using finite mixture of mixtures
In many modern applications, there is interest in analyzing enormous dat...
read it

Distributed Bayesian clustering
In many modern applications, there is interest in analyzing enormous dat...
read it

Nearest Neighbor Dirichlet Process
There is a rich literature on Bayesian nonparametric methods for unknown...
read it

Reproducible Bootstrap Aggregating
Heterogeneity between training and testing data degrades reproducibility...
read it

Diffusion based Gaussian process regression via heat kernel reconstruction
We propose an algorithm for Gaussian Process regression on an unknown em...
read it

Batch correction of highdimensional data
Biomedical research often produces highdimensional data confounded by b...
read it

Autoencoding graphvalued data with applications to brain connectomes
Our interest focuses on developing statistical methods for analysis of b...
read it

Identifying main effects and interactions among exposures using Gaussian processes
This article is motivated by the problem of studying the joint effect of...
read it

Soft Tensor Regression
Statistical methods relating tensor predictors to scalar outcomes in a r...
read it

Perturbed factor analysis: Improving generalizability across studies
Factor analysis is routinely used for dimensionality reduction. However,...
read it

Approximating posteriors with highdimensional nuisance parameters via integrated rotated Gaussian approximation
Posterior computation for highdimensional data with many parameters can...
read it

Estimating densities with nonlinear support using FisherGaussian kernels
Current tools for multivariate density estimation struggle when the dens...
read it

Bayesian inferences on uncertain ranks and orderings
It is common to be interested in rankings or order relationships among e...
read it

Geodesic Distance Estimation with Spherelets
Many statistical and machine learning approaches rely on pairwise distan...
read it

Monte Carlo simulation on the Stiefel manifold via polar expansion
Motivated by applications to Bayesian inference for statistical models w...
read it

Bayesian timealigned factor analysis of paired multivariate time series
Many modern data sets require inference methods that can estimate the sh...
read it

Bayesian Factor Analysis for Inference on Interactions
This article is motivated by the problem of inference on interactions am...
read it

Lipschitz Bandit Optimization with Improved Efficiency
We consider the Lipschitz bandit optimization problem with an emphasis o...
read it

Efficient Entropy Estimation for Stationary Time Series
Entropy estimation, due in part to its connection with mutual informatio...
read it

Classification via local manifold approximation
Classifiers label data as belonging to one of a set of groups based on i...
read it

Bayesian cumulative shrinkage for infinite factorizations
There are a variety of Bayesian models relying on representations in whi...
read it

Centered Partition Process: Informative Priors for Clustering
There is a very rich literature proposing Bayesian approaches for cluste...
read it

Multivariate mixed membership modeling: Inferring domainspecific risk profiles
Characterizing shared membership of individuals in two or more categorie...
read it

Nonparametric graphical model for counts
Although multivariate count data are routinely collected in many applica...
read it

Supervised CoarseGraining of Composite Objects
We consider supervised dimension reduction for regression with composite...
read it

Constrained Bayesian Inference through Posterior Projections
In a broad variety of settings, prior information takes the form of para...
read it

ClusteringEnhanced Stochastic Gradient MCMC for Hidden Markov Models with Rare States
MCMC algorithms for hidden Markov models, which often rely on the forwar...
read it

Bayesian Distance Clustering
Modelbased clustering is widelyused in a variety of application areas....
read it

Removing the influence of a group variable in highdimensional predictive modelling
Predictive modelling relies on the assumption that observations used for...
read it

Random orthogonal matrices and the Cayley transform
Random orthogonal matrices play an important role in probability and sta...
read it

Bayesian Modular and Multiscale Regression
We tackle the problem of multiscale regression for predictors that are s...
read it

Bayesian Nonparametric Higher Order Hidden Markov Models
We consider the problem of flexible modeling of higher order hidden Mark...
read it