11:30 am–12:30 pm
Jones 303 5747 S. Ellis Ave.
David Blei
Departments of Statistics and Computer Science
Columbia University
Title: A Fresh Look at Empirical Bayes
Abstract: Empirical Bayes improves simultaneous inference by learning fromrelated data. In this talk, I will present three recent directions in empirical Bayes. First, I will discuss a general method based on probabilistic symmetries, which extends empirical Bayes beyond exchangeable settings to structured problems such as arrays, graphs, conditional data, and spatial models. Second, I will discuss empirical
Bayes for implicit likelihoods, where the model is available only through a simulator, and show how simulation-based inference can be used to produce empirical Bayes estimates without evaluating a
density. Third, I will discuss an empirical Bayes approach to combining randomized experiments and observational studies, where calibration studies make it possible to learn the distribution of observational bias and use observational data in a principled way. These three ideas illustrate new roles for empirical Bayes in modern statistics and machine learning.
This is joint work with Diana Cai, Don Green, Sebastian Salazar, Xinwei Shen Sebastian Wagner-Carena, Bohan Wu, Cheng Zhang.
Bio: David Blei is the William B. Ransford Professor of Statistics and Computer Science at Columbia University. He studies probabilistic machine learning and Bayesian statistics, including theory, algorithms, and application. David has received several awards for his research, including the ACM Prize in Computing (2013), a Guggenheim fellowship (2017), a Simons Investigator Award (2019), the AAAI John McCarthy award (2024), and the ACM/AAAI Allan Newell Award (2024). He was the co-editor-in-chief of the Journal of Machine Learning Research from 2019-2024. He is a fellow of the Association for Computing Machinery (ACM) and the Institute of Mathematical Statistics (IMS).