100 citations · 206 across the 20 of their papers we have counts for
22 papers
Structured Latent Modeling for Supervised Multimodal Information Decomposition
Wanting Huang, Sanvesh Srivastava, Weiran Wang
Multimodal prediction relies on diverse forms of evidence: information repeated across modalities, cues specific to a single source, and complex cross-modal dependencies that emerg…
Sparse Separable Factor Analysis in the Complex Domain with an Application to Local Field Potential Data
Ian Hultman, Kirtikanth Kalapatapu, Yassine Filali +2
Complex-valued arrays arise in signal processing, where scientific interpretation depends on retaining amplitude and phase information. Existing covariance estimation methods eithe…
Moment bounds for condition numbers and singular values of high-dimensional Gaussian random matrices: Applications and limitations
Partha Sarkar, Kshitij Khare, Sanvesh Srivastava
Spectral properties of Gram matrices are central to high dimensional asymptotic analyses of statistical estimators in regression and covariance estimation. These properties, in tur…
CoMET: A Compressed Bayesian Mixed-Effects Model for High-Dimensional Tensors
Sreya Sarkar, Kshitij Khare, Sanvesh Srivastava
Mixed-effects models are fundamental tools for analyzing clustered and repeated-measures data, but existing high-dimensional methods largely focus on penalized estimation with vect…
Asynchronous Distributed ECME Algorithm for Matrix Variate Non-Gaussian Responses
Qingyang Liu, Sanvesh Srivastava, Dipankar Bandyopadhyay
We propose a regression model with matrix-variate skew-t response (REGMVST) for analyzing irregular longitudinal data with skewness, symmetry, or heavy tails. REGMVST models matrix…
SLIM-LLMs: Modeling of Style-Sensory Language RelationshipsThrough Low-Dimensional Representations
Osama Khalid, Sanvesh Srivastava, Padmini Srinivasan
Sensorial language -- the language connected to our senses including vision, sound, touch, taste, smell, and interoception, plays a fundamental role in how we communicate experienc…
Regularized Parameter Estimation in Mixed Model Trace Regression
Ian Hultman, Sanvesh Srivastava
We introduce mixed model trace regression (MMTR), a mixed model linear regression extension for scalar responses and high-dimensional matrix-valued covariates. MMTR's fixed effects…
Machine Learning and the Future of Bayesian Computation
Steven Winter, Trevor Campbell, Lizhen Lin +2
Bayesian models are a powerful tool for studying complex data, allowing the analyst to encode rich hierarchical dependencies and leverage prior information. Most importantly, they…
Asynchronous and Distributed Data Augmentation for Massive Data Settings
Jiayuan Zhou, Kshitij Khare, Sanvesh Srivastava
Data augmentation (DA) algorithms are widely used for Bayesian inference due to their simplicity. In massive data settings, however, DA algorithms are prohibitively slow because th…
Gaussian Process Regression and Classification using International Classification of Disease Codes as Covariates
Sanvesh Srivastava, Zongyi Xu, Yunyi Li +2
International Classification of Disease (ICD) codes are widely used for encoding diagnoses in electronic health records (EHR). Automated methods have been developed over the years…
Divide-and-Conquer Bayesian Inference in Hidden Markov Models
Chunlei Wang, Sanvesh Srivastava
Divide-and-conquer Bayesian methods consist of three steps: dividing the data into smaller computationally manageable subsets, running a sampling algorithm in parallel on all the s…
Asymptotic Normality of the Posterior Distributions in a Class of Hidden Markov Models
Chunlei Wang, Sanvesh Srivastava
We show that the posterior distribution of parameters in a hidden Markov model with parametric emission distributions and discrete and known state space is asymptotically normal. T…
Distributed Bayesian Varying Coefficient Modeling Using a Gaussian Process Prior
Rajarshi Guhaniyogi, Cheng Li, Terrance D. Savitsky +1
Varying coefficient models (VCMs) are widely used for estimating nonlinear regression functions for functional data. Their Bayesian variants using Gaussian process priors on the fu…
Pseudo Bayesian Estimation of One-way ANOVA Model in Complex Surveys
Terrance D. Savitsky, Matthew R. Williams, Sanvesh Srivastava
We devise survey-weighted pseudo posterior distribution estimators under two-stage informative sampling of both primary clusters and secondary nested units for a one-way analysis o…
An Algorithm for Distributed Bayesian Inference in Generalized Linear Models
Nariankadu D. Shyamalkumar, Sanvesh Srivastava
Monte Carlo algorithms, such as Markov chain Monte Carlo (MCMC) and Hamiltonian Monte Carlo (HMC), are routinely used for Bayesian inference in generalized linear models; however,…
An Asynchronous Distributed Expectation Maximization Algorithm For Massive Data: The DEM Algorithm
Sanvesh Srivastava, Glen DePalma, Chuanhai Liu
The family of Expectation-Maximization (EM) algorithms provides a general approach to fitting flexible models for large and complex data. The expectation (E) step of EM-type algori…
A Divide-and-Conquer Bayesian Approach to Large-Scale Kriging
Rajarshi Guhaniyogi, Cheng Li, Terrance D. Savitsky +1
We propose a three-step divide-and-conquer strategy within the Bayesian paradigm that delivers massive scalability for any spatial process model. We partition the data into a large…
Scalable Bayes under Informative Sampling
Terrance D. Savitsky, Sanvesh Srivastava
The United States Bureau of Labor Statistics collects data using survey instruments under informative sampling designs that assign probabilities of inclusion to be correlated with…
Simple, Scalable and Accurate Posterior Interval Estimation
Cheng Li, Sanvesh Srivastava, David B. Dunson
There is a lack of simple and scalable algorithms for uncertainty quantification. Bayesian methods quantify uncertainty through posterior and predictive distributions, but it is di…
Scalable Bayes via Barycenter in Wasserstein Space
Sanvesh Srivastava, Cheng Li, David B. Dunson
Divide-and-conquer based methods for Bayesian inference provide a general approach for tractable posterior inference when the sample size is large. These methods divide the data in…
Expandable Factor Analysis
Sanvesh Srivastava, Barbara E. Engelhardt, David B. Dunson
Bayesian sparse factor models have proven useful for characterizing dependence in multivariate data, but scaling computation to large numbers of samples and dimensions is problemat…
Robust and Scalable Bayes via a Median of Subset Posterior Measures
Stanislav Minsker, Sanvesh Srivastava, Lizhen Lin +1
We propose a novel approach to Bayesian analysis that is provably robust to outliers in the data and often has computational advantages over standard methods. Our technique is base…