113 citations · 294 across the 25 of their papers we have counts for
5 papers · 1 filter
Tuning Stochastic Gradient Algorithms for Statistical Inference via Large-Sample Asymptotics
Jeffrey Negrea, Jun Yang, Haoyue Feng +2
The tuning of stochastic gradient algorithms (SGAs) for optimization and sampling is often based on heuristics and trial-and-error rather than generalizable theory. We address this…
LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations
Brian L. Trippe, Jonathan H. Huggins, Raj Agrawal +1
Due to the ease of modern data collection, applied statisticians often have access to a large set of covariates that they wish to relate to some observed outcome. Generalized linea…
The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions
Raj Agrawal, Jonathan H. Huggins, Brian Trippe +1
Discovering interaction effects on a response of interest is a fundamental problem faced in biology, medicine, economics, and many other scientific disciplines. In theory, Bayesian…
PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference
Jonathan H. Huggins, Ryan P. Adams, Tamara Broderick
Generalized linear models (GLMs) -- such as logistic regression, Poisson regression, and robust regression -- provide interpretable models for diverse data types. Probabilistic app…
Coresets for Scalable Bayesian Logistic Regression
Jonathan H. Huggins, Trevor Campbell, Tamara Broderick
The use of Bayesian methods in large-scale data settings is attractive because of the rich hierarchical models, uncertainty quantification, and prior specification they provide. St…