Author

Rajarshi Guhaniyogi

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Preprint Sep 2026

Additive Nonparametric Regression with Spatial and Network Objects

This article is motivated by an imaging application from the Adolescent Brain Cognitive Development (ABCD) study, aiming to predict task-based brain activation maps from t-fMRI using cortical metrics from structural MRI (s-MRI) and brain connectivity data from resting-state fMRI (rs-fMRI). Hierarchical Bayesian modelin...

Rajarshi Guhaniyogi, Pritam Dey, Krishnendu Chandra et al. · 0 citations

Preprint Sep 2026

MCMC-Free Uncertainty Quantification for Deep Generative Inference for Spatially Varying Coefficient Models at Scale

Varying coefficient (VC) regression models have become indispensable tools in spatial data analysis, providing unmatched flexibility in capturing complex, nonlinear relationships and spatially-varying effects of predictors on responses. Although hierarchical Bayesian approaches offer a rigorous probabilistic framework...

Yeseul Jeon, A. Scheffler, Rajarshi Guhaniyogi · 0 citations

#machine learning Preprint Sep 2026

A Bayesian Vertical Federated Learning Framework for Multivariate Reduced-Rank High-Dimensional Regression

Federated learning (FL) has emerged as a leading privacy-preserving framework for collaborative machine learning across decentralized environments. While considerable progress has been made in horizontal federated learning (HFL), where data with common features is distributed across sites, vertical federated learning (...

Brigham Halverson, Sharmistha Guha, Jessica A. Bernard et al. · 0 citations