Andrew Gelman

Publication Activity (10 Years)

Years Active: 1988-2023
Publications (10 Years): 29

Top Topics

Variational Inference

Exploratory Data Analysis

Posterior Distribution

Top Venues

J. Mach. Learn. Res.

Publications

Thomas Ward, Mitzi Morris, Andrew Gelman, Bob Carpenter, William Ferguson, Christopher E. Overton, Martyn Fyles
Bayesian spatial modelling of localised SARS-CoV-2 transmission through mobility networks across England. PLoS Comput. Biol. 19 (11) (2023)
Han Guo, Philip Greengard, Hongyi Wang, Andrew Gelman, Yoon Kim, Eric P. Xing
Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach. ICLR (2023)
Anna L. Smith, Tian Zheng, Andrew Gelman
Prediction scoring of data-driven discoveries for reproducible research. Stat. Comput. 33 (1) (2023)
Jessica Hullman, Ari Holtzman, Andrew Gelman
Artificial Intelligence and Aesthetic Judgment. CoRR (2023)
Han Guo, Philip Greengard, Hongyi Wang, Andrew Gelman, Yoon Kim, Eric P. Xing
Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach. CoRR (2023)
Jessica Hullman, Sayash Kapoor, Priyanka Nanayakkara, Andrew Gelman, Arvind Narayanan
The Worst of Both Worlds: A Comparative Analysis of Errors in Learning from Data in Psychology and Machine Learning. AIES (2022)
Jessica Hullman, Sayash Kapoor, Priyanka Nanayakkara, Andrew Gelman, Arvind Narayanan
The worst of both worlds: A comparative analysis of errors in learning from data in psychology and machine learning. CoRR (2022)
Yuling Yao, Aki Vehtari, Andrew Gelman
Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors. J. Mach. Learn. Res. 23 (2022)
Gwynn Sturdevant, A. Jonathan R. Godfrey, Andrew Gelman
Delivering data differently. CoRR (2022)
Philip Greengard, Andrew Gelman, Aki Vehtari
A fast regression via SVD and marginalization. Comput. Stat. 37 (2) (2022)
Lu Zhang, Bob Carpenter, Andrew Gelman, Aki Vehtari
Pathfinder: Parallel quasi-Newton variational inference. J. Mach. Learn. Res. 23 (2022)
Tamara Broderick, Andrew Gelman, Rachael Meager, Anna L. Smith, Tian Zheng
Toward a Taxonomy of Trust for Probabilistic Machine Learning. CoRR (2021)
Lu Zhang, Bob Carpenter, Andrew Gelman, Aki Vehtari
Pathfinder: Parallel quasi-Newton variational inference. CoRR (2021)
Darren B. Glass, Béla Bajnok, Carolyn Yackel, Edray Goins, Jenna P. Carpenter, Stephen Kennedy, Jeanette Shakalli, Ursula Whitcher, Dave Kung, Kathryn Kozak, Robert Ghrist, Andrew Gelman
Reviews. Am. Math. Mon. 128 (2) (2021)
Jessica Hullman, Andrew Gelman
To design interfaces for exploratory data analysis, we need theories of graphical inference. CoRR (2021)
Yuling Yao, Gregor Pirs, Aki Vehtari, Andrew Gelman
Bayesian hierarchical stacking. CoRR (2021)
Jon Zelner, Julien Riou, Ruth Etzioni, Andrew Gelman
Accounting for uncertainty during a pandemic. Patterns 2 (8) (2021)
Aki Vehtari, Andrew Gelman, Tuomas Sivula, Pasi Jylänki, Dustin Tran, Swupnil Sahai, Paul Blomstedt, John P. Cunningham, David Schiminovich, Christian P. Robert
Expectation Propagation as a Way of Life: A Framework for Bayesian Inference on Partitioned Data. J. Mach. Learn. Res. 21 (2020)
Philip Greengard, Andrew Gelman, Aki Vehtari
A Fast Linear Regression via SVD and Marginalization. CoRR (2020)
Yuling Yao, Aki Vehtari, Daniel Simpson, Andrew Gelman
Yes, but Did It Work?: Evaluating Variational Inference. ICML (2018)
Aki Vehtari, Andrew Gelman, Jonah Gabry
Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat. Comput. 27 (5) (2017)
Shravan Vasishth, Andrew Gelman
The statistical significance filter leads to overconfident expectations of replicability. CogSci (2017)
Aki Vehtari, Andrew Gelman, Jonah Gabry
Erratum to: Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat. Comput. 27 (5) (2017)
Alp Kucukelbir, Dustin Tran, Rajesh Ranganath, Andrew Gelman, David M. Blei
Automatic Differentiation Variational Inference. J. Mach. Learn. Res. 18 (2017)
Andrew Gelman, Daniel Simpson, Michael Betancourt
The Prior Can Often Only Be Understood in the Context of the Likelihood. Entropy 19 (10) (2017)
Matilde Trevisani, Jie Shen, Alexander van Geen, Andrew Gelman, Shuky Ehrenberg, John Immel
A Safe Depth Forecasting Model for Insuring Tubewell Installations Against Arsenic Risk in Bangladesh. ICCSA (5) (2017)
Alp Kucukelbir, Dustin Tran, Rajesh Ranganath, Andrew Gelman, David M. Blei
Automatic Differentiation Variational Inference. CoRR (2016)
Alp Kucukelbir, Rajesh Ranganath, Andrew Gelman, David M. Blei
Automatic Variational Inference in Stan. NIPS (2015)
Ying Liu, Andrew Gelman, Tian Zheng
Simulation-efficient shortest probability intervals. Stat. Comput. 25 (4) (2015)
Andrew Gelman, Jessica Hwang, Aki Vehtari
Understanding predictive information criteria for Bayesian models. Stat. Comput. 24 (6) (2014)
David Rothschild, Sharad Goel, Andrew Gelman, Doug Rivers
The Mythical Swing Voter. CoRR (2014)
Matthew D. Hoffman, Andrew Gelman
The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. J. Mach. Learn. Res. 15 (1) (2014)
Nicolas Chopin, Andrew Gelman, Kerrie L. Mengersen, Christian P. Robert
In praise of the referee CoRR (2012)
Matthew D. Hoffman, Andrew Gelman
The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo CoRR (2011)
Andrew Gelman
Commentary - Discussion of the Article "Website Morphing". Mark. Sci. 28 (2) (2009)
Cavan Reilly, Andrew Gelman
Weighted Classical Variogram Estimation for Data With Clustering. Technometrics 49 (2) (2007)
Jouni Kerman, Andrew Gelman
Manipulating and summarizing posterior simulations using random variable objects. Stat. Comput. 17 (3) (2007)
Andrew Gelman, Iain Pardoe
Bayesian Measures of Explained Variance and Pooling in Multilevel (Hierarchical) Models. Technometrics 48 (2) (2006)
Alessandra Casella, Andrew Gelman, Thomas R. Palfrey
An experimental study of storable votes. Games Econ. Behav. 57 (1) (2006)
Andrew Gelman
Multilevel (Hierarchical) Modeling: What It Can and Cannot Do. Technometrics 48 (3) (2006)
Andrew Gelman, Francis Tuerlinckx
Type S error rates for classical and Bayesian single and multiple comparison procedures. Comput. Stat. 15 (3) (2000)
Iwin Leenen, Iven Van Mechelen, Andrew Gelman
Bayesian probabilistic extensions of a deterministic classification model. Comput. Stat. 15 (3) (2000)
Andrew Gelman, Susan Altman, Matt Pallakoff, Ketan Doshi, Catherine Manago, Thomas C. Rindfleisch, Bruce G. Buchanan
FRM: An Intelligent Assistant for Financial Resource Management. AAAI (1988)