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<div>ETH Foundations of Data Science (ETH-FDS), in association with ETH AI Center, announces and invites you to the following talk in our seminar series:</div>
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<div>„<b>A Fresh Look at Empirical Bayes</b>“</div>
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<div>by <span style="font-weight: bold;">David M. Blei, Columbia University</span></div>
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<div><b>Date and Time (Zurich)</b>: Thursday, 25 June 2026, 16:15 - 17.15</div>
<div><b>Place: </b> HG D 1.2</div>
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<div><b>Abstract</b>: 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 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, Eli Weinstein, Bohan Wu, Cheng Zhang.</div>
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<div>Seminar websites: https://math.ethz.ch/sfs/news-and-events/data-science-seminar.html, https://math.ethz.ch/sfs/eth-foundations-of-data-science.html</div>
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<div>Organisers: A. Bandeira, H. Bölcskei, P. Bühlmann, J. Peters, F. Yang</div>
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