25 citations · 82 across the 24 of their papers we have counts for
26 papers
MCMC-Free Uncertainty Quantification for Deep Generative Inference for Spatially Varying Coefficient Models at Scale
Yeseul Jeon, Aaron Scheffler, Rajarshi Guhaniyogi
Varying coefficient (VC) regression models have become indispensable tools in spatial data analysis, providing unmatched flexibility in capturing complex, nonlinear relationships a…
Marginally Correct Tool Caches Can Reverse Group-Normalized Policy Updates
Shivam Gupta
Tool-result caching reduces repeated execution in agent training, but also couples rollout randomness. We study a two-action model in which independent and shared execution preserv…
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…
JASPER: Joint Bayesian Analysis of Spatial Expression via Regression
Pritam Dey, Rajarshi Guhaniyogi, Yang Ni +1
Spatially resolved transcriptomics is a fast-developing set of technologies that enables the measurement of localized gene expression across spatial locations in a sample. Detectin…
Uncertainty-Aware Neural Multivariate Geostatistics
Yeseul Jeon, Aaron Scheffler, Rajarshi Guhaniyogi
We propose Deep Neural Coregionalization, a scalable framework for uncertainty-aware multivariate geostatistics. DNC models multivariate spatial effects through spatially varying l…
Supervised Learning of Functional Outcomes with Predictors at Different Scales: A Functional Gaussian Process Approach
R. Jacob Andros, Rajarshi Guhaniyogi, Devin Francom +1
The analysis of complex computer simulations, often involving functional data, presents unique statistical challenges. Conventional regression methods, such as function-on-function…
Mapping Drivers of Greenness: Spatial Variable Selection for MODIS Vegetation Indices
Qishi Zhan, Cheng-Han Yu, Yuchi Chen +2
Understanding how environmental drivers relate to vegetation condition motivates spatially varying regression models, but estimating a separate coefficient surface for every predic…
Adaptive Divide and Conquer with Two Rounds of Communication
Niladri Kal, Botond Szabó, Rajarshi Guhaniyogi +2
We introduce a two-round adaptive communication strategy that enables rate-optimal estimation in the white noise model without requiring prior knowledge of the underlying smoothnes…
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…
Deep Generative Modeling with Spatial and Network Images: An Explainable AI (XAI) Approach
Yeseul Jeon, Rajarshi Guhaniyogi, Aaron Scheffler
This article addresses the challenge of modeling the amplitude of spatially indexed low frequency fluctuations (ALFF) in resting state functional MRI as a function of cortical stru…
Interpretable Deep Neural Network for Modeling Functional Surrogates
Yeseul Jeon, Rajarshi Guhaniyogi, Aaron Scheffler +2
Developing surrogates for computer models has become increasingly important for addressing complex problems in science and engineering. This article introduces an artificial intell…
Multi-object Data Integration in the Study of Primary Progressive Aphasia
Rene Gutierrez, Rajarshi Guhaniyogi, Aaron Scheffler +3
This article focuses on a multi-modal imaging data application where structural/anatomical information from gray matter (GM) and brain connectivity information in the form of a bra…
Robust Distributed Learning of Functional Data From Simulators through Data Sketching
R. Jacob Andros, Rajarshi Guhaniyogi, Devin Francom +1
In environmental studies, realistic simulations are essential for understanding complex systems. Statistical emulation with Gaussian processes (GPs) in functional data models have…
Data Sketching and Stacking: A Confluence of Two Strategies for Predictive Inference in Gaussian Process Regressions with High-Dimensional Features
Samuel Gailliot, Rajarshi Guhaniyogi, Niladri Kal +1
This article focuses on drawing computationally-efficient predictive inference from Gaussian process (GP) regressions with a large number of features when the response is condition…
Integrative Variational Autoencoders for Generative Modeling of an Image Outcome with Multiple Input Images
Bowen Lei, Yeseul Jeon, Rajarshi Guhaniyogi +3
Understanding relationships across multiple imaging modalities is central to neuroimaging research. We introduce the Integrative Variational Autoencoder (InVA), the first hierarchi…
Bayesian tensor regression using the Tucker decomposition for sparse spatial modeling
Daniel Spencer, Rajarshi Guhaniyogi, Russell Shinohara +1
Modeling with multidimensional arrays, or tensors, often presents a problem due to high dimensionality. In addition, these structures typically exhibit inherent sparsity, requiring…
Sketching in Bayesian High Dimensional Regression With Big Data Using Gaussian Scale Mixture Priors
Rajarshi Guhaniyogi, Aaron Scheffler
Bayesian computation of high dimensional linear regression models with a popular Gaussian scale mixture prior distribution using Markov Chain Monte Carlo (MCMC) or its variants can…
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…
Bayesian Mixed Effect Sparse Tensor Response Regression Model with Joint Estimation of Activation and Connectivity
Daniel Spencer, Rajarshi Guhaniyogi, Raquel Prado
Brain activation and connectivity analyses in task-based functional magnetic resonance imaging (fMRI) experiments with multiple subjects are currently at the forefront of data-driv…
Large Multi-scale Spatial Kriging Using Tree Shrinkage Priors
Rajarshi Guhaniyogi, Bruno Sanso
We develop a multiscale spatial kernel convolution technique with higher order functions to capture fine scale local features and lower order terms to capture large scale features.…
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…
A Case Study Competition Among Methods for Analyzing Large Spatial Data
Matthew J. Heaton, Abhirup Datta, Andrew Finley +10
The Gaussian process is an indispensable tool for spatial data analysts. The onset of the "big data" era, however, has lead to the traditional Gaussian process being computationall…
Bayesian Tensor Regression
Rajarshi Guhaniyogi, Shaan Qamar, David B. Dunson
This article proposes a Bayesian approach to regression with a scalar response against vector and tensor covariates. Tensor covariates are commonly vectorized prior to analysis, fa…
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…