Hosted as part of the Machine Learning and AI Seminar Series in partnership with the DSI Foundations of Data Science Center; the Department of Statistics, Arts and Sciences; and Columbia Engineering

Speaker

Francis Bach, Senior Researcher at INRIA, SIERRA Project-Team Leader, Computer Science Department, École Normale Supérieure

Event Details

Friday, May 15, 2026 (11:00 AM – 12:00 PM ET)

Location: Hamilton Hall, Room 702

REGISTRATION DEADLINE: The Columbia Morningside campus is open to the Columbia community. If you do not have an active CUID, the deadline to register is at 12:00 PM the day before the event.

Register

Talk Information

Recent Advances in Uncertainty Quantification: Anytime Guarantees and Multivariate Predictions

Abstract: Quantifying uncertainty in statistics and machine learning is crucial, but challenging in high-dimensional prediction problems. Probabilistic calibration and conformal prediction have emerged as key practical theoretically well-motivated frameworks. In this talk, I will present recent advances that allow greater flexibility in their applications, in terms of anytime guarantees and applications in multivariate prediction problems beyond univariate regression and binary classification.