1 citations · 2 across the 8 of their papers we have counts for
8 papers
Integrating Neural Encoders in Bayesian Generalized Linear Mixed Models for Multimodal Data
Yuankang Zhao, Youngsoo Baek, Felipe A. Medeiros +2
Scalable Bayesian inference for generalized linear mixed models (GLMMs) provides uncertainty-aware analysis of correlated longitudinal data, but existing scalable approaches largel…
Scalable and Calibrated Sampling for Bayesian Generalized Linear Mixed Model via Stochastic Gradient Markov Chain Monte Carlo
Youngsoo Baek, Andrea Agazzi, Felipe A. Medeiros +1
Generalized linear mixed models (GLMMs) are widely used for analyzing correlated data, particularly in large-scale biomedical and social science applications. Scalable Bayesian inf…
Asymptotics of Bayesian Uncertainty Estimation in Random Features Regression
Youngsoo Baek, Samuel I. Berchuck, Sayan Mukherjee
In this paper we compare and contrast the behavior of the posterior predictive distribution to the risk of the maximum a posteriori estimator for the random features regression mod…
Neural Conditional Event Time Models
Matthew Engelhard, Samuel Berchuck, Joshua D'Arcy +1
Event time models predict occurrence times of an event of interest based on known features. Recent work has demonstrated that neural networks achieve state-of-the-art event time pr…
Bayesian Non-Parametric Factor Analysis for Longitudinal Spatial Surfaces
Samuel I. Berchuck, Mark Janko, Felipe A. Medeiros +2
We introduce a Bayesian non-parametric spatial factor analysis model with spatial dependency induced through a prior on factor loadings. For each column of the loadings matrix, spa…
Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma
Samuel I. Berchuck, Felipe A. Medeiros, Sayan Mukherjee
As big spatial data becomes increasingly prevalent, classical spatiotemporal (ST) methods often do not scale well. While methods have been developed to account for high-dimensional…
A spatially varying change points model for monitoring glaucoma progression using visual field data
Samuel I. Berchuck, Jean-Claude Mwanza, Joshua L. Warren
Glaucoma disease progression, as measured by visual field (VF) data, is often defined by periods of relative stability followed by an abrupt decrease in visual ability at some poin…
Diagnosing Glaucoma Progression with Visual Field Data Using a Spatiotemporal Boundary Detection Method
Samuel I. Berchuck, Jean-Claude Mwanza, Joshua L. Warren
Diagnosing glaucoma progression is critical for limiting irreversible vision loss. A common method for assessing glaucoma progression uses a longitudinal series of visual fields (V…