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Colloquium
Finding Stationary Points in Stochastic Convex Optimization: Why and How
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Speaker: John Duchi (Stanford) Associate Professor of Statistics and Electrical Engineering Stanford University Wednesday, September 16, 2026 11:30AM - 1:00PM Lunch in room 1307, 11:00-11:30am
Talk in room 1327, 12:00-1:00pm Location: Yale Institute for Foundations of Data Science, Kline Tower 13th Floor, Room 1327, New Haven, CT 06511 and via Webcast: https://yale.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=42cda81c-ca5d-46d1-8bdd-b4b00127bac6 |
Abstract: In this talk, I will discuss finding stationary points of possibly non-differentiable stochastic convex functions. Motivation for solving such problems arises in modern “distribution free” statistical learning problems. We will highlight some of the challenges of solving such problems, including that subgradients of stochastic convex functions do not converge uniformly, and also demonstrate some new geometric tools to show that solving the problem is in fact possible.
Based on the paper “Finding a stationary point of a stochastic convex problem” (arXiv:2607.06883), a joint work with Felipe Areces and Malo Sommers.
Speaker Bio: John Duchi is an associate professor of Statistics and Electrical Engineering at Stanford University. His work spans statistical learning, optimization, information theory, and computation, with a few driving goals. (1) To discover statistical learning procedures that optimally trade between real-world resources — computation, communication, privacy provided to study participants — while maintaining statistical efficiency. (2) To build efficient large-scale optimization methods that address the spectrum of optimization, machine learning, and data analysis problems we face, allowing us to move beyond bespoke solutions to methods that robustly work. (3) To develop tools to assess and guarantee the validity of — and confidence we should have in — machine-learned systems.
Faculty Host: Ilias Zadik
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