Data Science Colloquium

of the ENS

Welcome to the Data Science Colloquium of the ENS.

This colloquium is organized around data sciences in a broad sense with the goal of bringing together researchers with diverse backgrounds (including for instance mathematics, computer science, physics, chemistry and neuroscience) but a common interest in dealing with large scale or high dimensional data.

The colloquium is followed by an open buffet around which participants can meet and discuss collaborations.

These seminars are made possible by the support of the CFM-ENS Chair “Modèles et Sciences des Données”.

You can check the list of the next seminars below and the list of past seminars.

Videos of some of the past seminars are available online.

Organizers

The colloquium is organized by:

Next seminars

08 January 2026, 12h00-13h00 (Paris time), room Amphi Jaurès (29 Rue d'Ulm).
Nathan Srebro (Toyota Technological Institute at Chicago)
Title: Learning to Answer from Correct Demonstrations
Abstract: We study the problem of learning to generate an answer (or completion) to a question (or prompt), where there could be multiple correct answers, any one of which is acceptable at test time. Learning is based on demonstrations of some correct answer to each training question, as in Supervised Fine Tuning (SFT). Current standard practice focuses on maximum likelihood (ie log loss minimization) approaches, but we argue that likelihood-maximization methods can fail even in simple settings. Instead, we view the problem as apprenticeship learning (i.e., imitation learning) in contextual bandits, with offline demonstrations from some expert (optimal, or very good) policy, and suggest alternative simple approaches with strong guarantees. Joint work with Nirmit Joshi, Gene Li, Siddharth Bhandari, Shiva Kasiviswanathan, and Cong Ma

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