25 citations · 82 across the 24 of their papers we have counts for
5 papers · 1 filter
A Bayesian Vertical Federated Learning Framework for Multivariate Reduced-Rank High-Dimensional Regression
Brigham Halverson, Sharmistha Guha, Jessica Bernard +1
Federated learning (FL) has emerged as a leading privacy-preserving framework for collaborative machine learning across decentralized environments. While considerable progress has…
Bayesian Data Sketching for Varying Coefficient Regression Models
Rajarshi Guhaniyogi, Laura Baracaldo, Sudipto Banerjee
Varying coefficient models are popular for estimating nonlinear regression functions in functional data models. Their Bayesian variants have received limited attention in large dat…
Compressed Gaussian Process
Rajarshi Guhaniyogi, David B. Dunson
Nonparametric regression for massive numbers of samples (n) and features (p) is an increasingly important problem. In big n settings, a common strategy is to partition the feature…
Bayesian Conditional Density Filtering
Shaan Qamar, Rajarshi Guhaniyogi, David B. Dunson
We propose a Conditional Density Filtering (C-DF) algorithm for efficient online Bayesian inference. C-DF adapts MCMC sampling to the online setting, sampling from approximations t…
Bayesian Compressed Regression
Rajarshi Guhaniyogi, David B. Dunson
As an alternative to variable selection or shrinkage in high dimensional regression, we propose to randomly compress the predictors prior to analysis. This dramatically reduces sto…