ETH-FDS seminar series

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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 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

Details

ETH-FDS seminar

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

13 March 2025
16:15-17:15

Yinyue Ye
Stanford, CUHKSZ, HKUST, and SJTU

Details

ETH-FDS seminar

Title	Mathematical Optimization in the Era of AI
Speaker, Affiliation	Yinyue Ye, Stanford, CUHKSZ, HKUST, and SJTU
Date, Time	13 March 2025, 16:15-17:15
Location	HG G 19.1
Abstract	This talk aims to present several mathematical optimization problems/algorithms for AI such as the LLM training, tunning and inferencing. In particular, we describe how classic optimization models/theories can be applied to accelerate and improve the Training/Tunning/Inferencing algorithms that are popularly used in LLMs. On the other hand, we show breakthroughs in classical Optimization (LP and SDP) Solvers aided by AI-related techniques such as first-order and ADMM methods, the low-rank SDP theories, and the GPU Implementations. Bio: Yinyu Ye is currently the K.T. Li Professor of Engineering at Department of Management Science and Engineering and Institute of Computational and Mathematical Engineering, Stanford University; and visiting chair professor of Shanghai Jiao Tong University. His current research topics include Continuous and Discrete Optimization, Data Science and Applications, Algorithm Design and Analyses, Algorithmic Game/Market Equilibrium, Operations Research and Management Science etc.; and he was one of the pioneers on Interior-Point Methods, Conic Linear Programming, Distributionally Robust Optimization, Online Linear Programming and Learning, Algorithm Analyses for Reinforcement Learning & Markov Decision Process and nonconvex optimization, and etc. He and his students have received numerous scientific awards, himself including the 2006 INFORMS Farkas Prize (Inaugural Recipient) for fundamental contributions to optimization, the 2009 John von Neumann Theory Prize for fundamental sustained contributions to theory in Operations Research and the Management Sciences, the inaugural 2012 ISMP Tseng Lectureship Prize for outstanding contribution to continuous optimization (every three years), the 2014 SIAM Optimization Prize awarded (every three years).

Mathematical Optimization in the Era of AIread_more

HG G 19.1

20 March 2025
16:15-17:15

Stefan Wager
Stanford University

Details

ETH-FDS seminar

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

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