23rd Armitage Workshop and Lecture

Keynote

"A Fresh Look at Empirical Bayes" – Professor David M. Blei, Professor of Statistics and Computer Science from Columbia University

Abstract

Empirical Bayes improves simultaneous inference by learning from related data. In this talk, I will present three recent directions in empirical Bayes. First, 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. 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 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. 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, Eli Weinstein, Bohan Wu, Cheng Zhang.

Speaker Biography

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).

Programme

The line-up of other speakers is being developed and will include talks from experts in the Biostatistical Machine Learning field. Final programme to be shared in due course.