ETH-FDS seminar series

On this website you can find information about upcoming and past seminar talks.

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Spring Semester 2025

Date / Time

Speaker

Title

Location

27 February 2025
16:15-17:15

David M. Blei
Columbia University

Details

ETH-FDS seminar

Title	Joint talk FDS/Research Seminar on Statistics: "Scaling and Generalizing Approximate Bayesian Inference"
Speaker, Affiliation	David M. Blei, Columbia University
Date, Time	27 February 2025, 16:15-17:15
Location
Abstract	A core problem in statistics and machine learning is to approximate difficult-to-compute probability distributions. This problem is especially important in Bayesian statistics, which frames all inference about unknown quantities as a calculation about a conditional distribution. In this talk I review and discuss innovations in variational inference (VI), a method that approximates probability distributions through optimization. VI has been used in myriad applications in machine learning and Bayesian statistics. After quickly reviewing the basics, I will discuss two lines ofresearch in VI. I first describe stochastic variational inference, an approximate inference algorithm for handling massive datasets, and demonstrate its application to probabilistic topic models of millions of articles. Then I discuss black box variational inference, a more generic algorithm for approximating the posterior. Black box inference applies to many models but requires minimal mathematical work to implement. I will demonstrate black box inference on deep exponential families---a method for Bayesian deep learning---and describe how it enables powerful tools for probabilistic programming. Finally, I will highlight some more recent results in variational inference, including statistical theory, score-based objective functions, and interpolating between mean-field and fully dependent variational families.

Joint talk FDS/Research Seminar on Statistics: "Scaling and Generalizing Approximate Bayesian Inference"read_more

7 March 2025
16:15-17:15

David M. Blei
Columbia University

Details

ETH-FDS seminar

Title	Joint talk FDS/Research Seminar on Statistics: "Hierarchical Causal Models"
Speaker, Affiliation	David M. Blei, Columbia University
Date, Time	7 March 2025, 16:15-17:15
Location	HG D 7.2
Abstract	Analyzing nested data with hierarchical models is a staple of Bayesian statistics, but causal modeling remains largely focused on "flat" models. In this talk, we will explore how to think about nested data in causal models, and we will consider the advantages of nested data over aggregate data (such as data means) for causal inference. We show that disaggregating your data---replacing a flat causal model with a hierarchical causal model---can provide new opportunities for identification and estimation. As examples, we will study how to identify and estimate causal effects under unmeasured confounders, interference, and instruments. Preprint: https://arxiv.org/abs/2401.05330 This is joint work with Eli Weinstein.

Joint talk FDS/Research Seminar on Statistics: "Hierarchical Causal Models"read_more

HG D 7.2

20 March 2025
16:15-17:15

Stefan Wager

Details

ETH-FDS seminar

Title	Joint talk FDS/Research Seminar on Statistics
Speaker, Affiliation	Stefan Wager,
Date, Time	20 March 2025, 16:15-17:15
Location	HG E 3
Abstract	tba

Joint talk FDS/Research Seminar on Statisticsread_more

HG E 3

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