Aller au contenu. | Aller à la navigation

Outils personnels

Navigation

UMR 5672

logo de l'ENS de Lyon
logo du CNRS
Vous êtes ici : Accueil / Séminaires / Machine Learning and Signal Processing / Implicit Bias in Gradient Flow Learning: Results, Open Questions, and Emerging Dynamics

Implicit Bias in Gradient Flow Learning: Results, Open Questions, and Emerging Dynamics

Loucas Pillaud-Vivien (assistant professor at Ecole des Ponts ParisTech, CERMICS)
Quand ? Le 27/01/2026,
de 13:00 à 14:00
Où ? M7 101
S'adresser à Cédric Gerbelot
Participants Loucas Pillaud-Vivien
Ajouter un événement au calendrier vCal
iCal

Loucas Pillaud-Vivien

Title:  Implicit Bias in Gradient Flow Learning: Results, Open Questions, and Emerging Dynamics

Abstract:  
In this talk, I will present several results on the estimator selected by gradient flow in overparameterized learning problems. This phenomenon, known as implicit bias, plays a central role in modern machine learning, as it governs generalization in the absence of explicit regularization.
Beyond classical settings where the implicit bias is now well understood, I will discuss a range of open questions arising in more complex models and learning regimes. In particular, I will focus on dynamical aspects of gradient-based learning, including saddle-to-saddle dynamics and connections with incremental learning, which reveal a richer and still largely unexplored structure of these systems.
 

Website: https://thebiglouloup.github.io/loucaspillaudvivien/

In Room M7 101, 1st floor, Monod campus, ENSL.