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Spring Semester 2025

Date / Time Speaker Title Location
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
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Research Seminar in Statistics

Title Joint talk ETH-FDS Seminar - Research Seminar on Statistics: "Optimal Mechanisms for Demand Response: An Indifference Set Approach"
Speaker, Affiliation Stefan Wager, Stanford University
Date, Time 20 March 2025, 16:15-17:15
Location HG E 3
Abstract The time at which renewable (e.g., solar or wind) energy resources produce electricity cannot generally be controlled. In many settings, consumers have some flexibility in their energy consumption needs, and there is growing interest in demand-response programs that leverage this flexibility to shift energy consumption to better match renewable production -- thus enabling more efficient utilization of these resources. We study optimal demand response in a model where consumers operate home energy management systems (HEMS) that can compute the "indifference set" of energy-consumption profiles that meet pre-specified consumer objectives, receive demand-response signals from the grid, and control consumer devices within the indifference set. For example, if a consumer asks for the indoor temperature to remain between certain upper and lower bounds, a HEMS could time use of air conditioning or heating to align with high renewable production when possible. Here, we show that while price-based mechanisms do not in general achieve optimal demand response, i.e., dynamic pricing cannot induce HEMS to choose optimal demand consumption profiles within the available indifference sets, pricing is asymptotically optimal in a mean-field limit with a growing number of consumers. Furthermore, we show that large-sample optimal dynamic prices can be efficiently derived via an algorithm that only requires querying HEMS about their planned consumption schedules given different prices. We demonstrate our approach in a grid simulation powered by OpenDSS, and show that it achieves meaningful demand response without creating grid instability. Mohammad Mehrabi, Omer Karaduman, Stefan Wager https://arxiv.org/abs/2409.07655
Joint talk ETH-FDS Seminar - Research Seminar on Statistics: "Optimal Mechanisms for Demand Response: An Indifference Set Approach"read_more
HG E 3
2 April 2025
15:15-16:00 		Linbo Wang
University of Toronto
Details

Research Seminar in Statistics

Title The synthetic instrument: From sparse association to sparse causation
Speaker, Affiliation Linbo Wang, University of Toronto
Date, Time 2 April 2025, 15:15-16:00
Location HG G 19.1
Abstract In many observational studies, researchers are often interested in studying the effects of multiple exposures on a single outcome. Standard approaches for high-dimensional data such as the lasso assume the associations between the exposures and the outcome are sparse. These methods, however, do not estimate the causal effects in the presence of unmeasured confounding. In this paper, we consider an alternative approach that assumes the causal effects in view are sparse. We show that with sparse causation, the causal effects are identifiable even with unmeasured confounding. At the core of our proposal is a novel device, called the synthetic instrument, that in contrast to standard instrumental variables, can be constructed using the observed exposures directly. We show that under linear structural equation models, the problem of causal effect estimation can be formulated as an ℓ0-penalization problem, and hence can be solved efficiently using off-the-shelf software. Simulations show that our approach outperforms state-of-art methods in both low-dimensional and high-dimensional settings. We further illustrate our method using a mouse obesity dataset.
The synthetic instrument: From sparse association to sparse causationread_more
HG G 19.1

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