
Diffusion Based Gaussian Processes on Restricted Domains
In nonparametric regression and spatial process modeling, it is common f...
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Bayesian Matrix Completion for Hypothesis Testing
The United States Environmental Protection Agency (EPA) screens thousand...
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Density estimation and modeling on symmetric spaces
In many applications, data and/or parameters are supported on nonEuclid...
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Extended Stochastic Block Models
Stochastic block models (SBM) are widely used in network science due to ...
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Robust Optimization and Inference on Manifolds
We propose a robust and scalable procedure for general optimization and ...
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A generalized Bayes framework for probabilistic clustering
Lossbased clustering methods, such as kmeans and its variants, are sta...
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Bayesian clustering of highdimensional data
In many applications, it is of interest to cluster subjects based on ver...
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Classification Trees for Imbalanced and Sparse Data: SurfacetoVolume Regularization
Classification algorithms face difficulties when one or more classes hav...
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Bayesian semiparametric long memory models for discretized event data
We introduce a new class of semiparametric latent variable models for lo...
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Composite mixture of loglinear models for categorical data
Multivariate categorical data are routinely collected in many applicatio...
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Distributed Bayesian clustering using finite mixture of mixtures
In many modern applications, there is interest in analyzing enormous dat...
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Distributed Bayesian clustering
In many modern applications, there is interest in analyzing enormous dat...
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Nearest Neighbor Dirichlet Process
There is a rich literature on Bayesian nonparametric methods for unknown...
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Reproducible Bootstrap Aggregating
Heterogeneity between training and testing data degrades reproducibility...
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Diffusion based Gaussian process regression via heat kernel reconstruction
We propose an algorithm for Gaussian Process regression on an unknown em...
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Batch correction of highdimensional data
Biomedical research often produces highdimensional data confounded by b...
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Autoencoding graphvalued data with applications to brain connectomes
Our interest focuses on developing statistical methods for analysis of b...
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Identifying main effects and interactions among exposures using Gaussian processes
This article is motivated by the problem of studying the joint effect of...
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Soft Tensor Regression
Statistical methods relating tensor predictors to scalar outcomes in a r...
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Perturbed factor analysis: Improving generalizability across studies
Factor analysis is routinely used for dimensionality reduction. However,...
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Approximating posteriors with highdimensional nuisance parameters via integrated rotated Gaussian approximation
Posterior computation for highdimensional data with many parameters can...
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Estimating densities with nonlinear support using FisherGaussian kernels
Current tools for multivariate density estimation struggle when the dens...
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Bayesian inferences on uncertain ranks and orderings
It is common to be interested in rankings or order relationships among e...
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Geodesic Distance Estimation with Spherelets
Many statistical and machine learning approaches rely on pairwise distan...
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Monte Carlo simulation on the Stiefel manifold via polar expansion
Motivated by applications to Bayesian inference for statistical models w...
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Bayesian timealigned factor analysis of paired multivariate time series
Many modern data sets require inference methods that can estimate the sh...
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Bayesian Factor Analysis for Inference on Interactions
This article is motivated by the problem of inference on interactions am...
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Lipschitz Bandit Optimization with Improved Efficiency
We consider the Lipschitz bandit optimization problem with an emphasis o...
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Efficient Entropy Estimation for Stationary Time Series
Entropy estimation, due in part to its connection with mutual informatio...
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Classification via local manifold approximation
Classifiers label data as belonging to one of a set of groups based on i...
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Bayesian cumulative shrinkage for infinite factorizations
There are a variety of Bayesian models relying on representations in whi...
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Centered Partition Process: Informative Priors for Clustering
There is a very rich literature proposing Bayesian approaches for cluste...
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Multivariate mixed membership modeling: Inferring domainspecific risk profiles
Characterizing shared membership of individuals in two or more categorie...
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Nonparametric graphical model for counts
Although multivariate count data are routinely collected in many applica...
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Supervised CoarseGraining of Composite Objects
We consider supervised dimension reduction for regression with composite...
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Constrained Bayesian Inference through Posterior Projections
In a broad variety of settings, prior information takes the form of para...
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ClusteringEnhanced Stochastic Gradient MCMC for Hidden Markov Models with Rare States
MCMC algorithms for hidden Markov models, which often rely on the forwar...
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Bayesian Distance Clustering
Modelbased clustering is widelyused in a variety of application areas....
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Removing the influence of a group variable in highdimensional predictive modelling
Predictive modelling relies on the assumption that observations used for...
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Random orthogonal matrices and the Cayley transform
Random orthogonal matrices play an important role in probability and sta...
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Bayesian Modular and Multiscale Regression
We tackle the problem of multiscale regression for predictors that are s...
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Bayesian Nonparametric Higher Order Hidden Markov Models
We consider the problem of flexible modeling of higher order hidden Mark...
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Multiresolution Tensor Decomposition for Multiple Spatial Passing Networks
This article is motivated by soccer positional passing networks collecte...
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Latent nested nonparametric priors
Discrete random structures are important tools in Bayesian nonparametric...
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Bayesian Constraint Relaxation
Prior information often takes the form of parameter constraints. Bayesia...
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Targeted Random Projection for Prediction from HighDimensional Features
We consider the problem of computationallyefficient prediction from hig...
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Bayesian Multi Plate High Throughput Screening of Compounds
High throughput screening of compounds (chemicals) is an essential part ...
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Efficient Manifold and Subspace Approximations with Spherelets
Data lying in a highdimensional ambient space are commonly thought to h...
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Boosting Variational Inference
Variational inference (VI) provides fast approximations of a Bayesian po...
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Variational Gaussian Copula Inference
We utilize copulas to constitute a unified framework for constructing an...
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