Research Seminar

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Spring Semester 2025

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27 February 2025
16:15-17:15

David M. Blei
Columbia University

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Research Seminar in Statistics

Title	Joint talk ETH-FDS Seminar - 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	HG D 1.2
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 of research 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 ETH-FDS Seminar - Research Seminar on Statistics:"Scaling and Generalizing Approximate Bayesian Inference"read_more

HG D 1.2

7 March 2025
16:15-17:15

David M. Blei
Columbia University

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Research Seminar in Statistics

Title	Joint talk ETH-FDS Seminar - 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 ETH-FDS Seminar - Research Seminar on Statistics:"Hierarchical Causal Models"read_more

HG D 7.2

14 March 2025
15:15-16:00

Matteo Fontana
Royal Holloway, University of London

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Research Seminar in Statistics

Title	Multi-Output Conformal Regression: A Unified View with Comparisons
Speaker, Affiliation	Matteo Fontana, Royal Holloway, University of London
Date, Time	14 March 2025, 15:15-16:00
Location	HG G 19.1
Abstract	Quantifying uncertainty in multivariate regression is crucial across many real-world applications. However, existing approaches for constructing prediction regions often struggle to capture complex dependencies, lack formal coverage guarantees, or incur high computational costs. Conformal prediction addresses these challenges by providing a robust, distribution-free framework with finite-sample coverage guarantees. In this study, we offer a unified comparison of multi-output conformal techniques, highlighting their properties and interrelationships. Leveraging these insights, we propose two families of conformity scores that achieve asymptotic conditional coverage: one can be paired with any generative model, while the other reduces computational overhead by utilizing invertible generative models. We then present a large-scale empirical analysis on 32 tabular datasets, comparing all methods under a consistent code base to ensure fairness and reproducibility.

Multi-Output Conformal Regression: A Unified View with Comparisonsread_more

HG G 19.1

20 March 2025
16:15-17:15

Stefan Wager
Stanford University

Details

Research Seminar in Statistics

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

Joint talk ETH-FDS Seminar - Research Seminar on Statisticsread_more

HG E 3

2 April 2025
15:15-16:00

Linbo Wang
University of Toronto

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Research Seminar in Statistics

Title	Title T.B.A.
Speaker, Affiliation	Linbo Wang, University of Toronto
Date, Time	2 April 2025, 15:15-16:00
Location	HG G 19.1
Abstract	tba

Title T.B.A.read_more

HG G 19.1

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