Gelman, Andrew

10 publications

JMLR 2024 Pareto Smoothed Importance Sampling Aki Vehtari, Daniel Simpson, Andrew Gelman, Yuling Yao, Jonah Gabry

ICLR 2023 Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach Han Guo, Philip Greengard, Hongyi Wang, Andrew Gelman, Yoon Kim, Eric Xing

JMLR 2022 Pathfinder: Parallel Quasi-Newton Variational Inference Lu Zhang, Bob Carpenter, Andrew Gelman, Aki Vehtari

JMLR 2022 Stacking for Non-Mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors Yuling Yao, Aki Vehtari, Andrew Gelman

JMLR 2020 Expectation Propagation as a Way of Life: A Framework for Bayesian Inference on Partitioned Data Aki Vehtari, Andrew Gelman, Tuomas Sivula, Pasi Jylänki, Dustin Tran, Swupnil Sahai, Paul Blomstedt, John P. Cunningham, David Schiminovich, Christian P. Robert

ICML 2018 Yes, but Did It Work?: Evaluating Variational Inference Yuling Yao, Aki Vehtari, Daniel Simpson, Andrew Gelman

JMLR 2017 Automatic Differentiation Variational Inference Alp Kucukelbir, Dustin Tran, Rajesh Ranganath, Andrew Gelman, David M. Blei

NeurIPS 2015 Automatic Variational Inference in Stan Alp Kucukelbir, Rajesh Ranganath, Andrew Gelman, David Blei

JMLR 2014 The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo Matthew D. Hoffman, Andrew Gelman

AAAI 1988 FRM: An Intelligent Assistant for Financial Resource Management Andrew Gelman, Susan Altman, Matt Pallakoff, Ketan Doshi, Catherine Manago, Thomas C. Rindfleisch, Bruce G. Buchanan