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Uniform convergence bounds via PAC-Bayes and Wasserstein distances

le 25 septembre 2024

13h15

Campus de Beaulieu Salle Jersey - bât. 12D

Intervention de Paul Viallard, chercheur Inria au centre Inria de l'Université de Rennes, dans le cadre des séminaires du département Informatique.

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In machine learning, practitioners may encounter overfitting when a model performs well on the training set but poorly on the task represented by the test set. One way to assess overfitting is through generalization bounds, which provide upper bounds on a model's performance for unseen tasks. In this talk, I will first review some basics of machine learning and discuss two types of bounds introduced in the literature: PAC-Bayesian bounds and uniform convergence bounds. Although these two types exhibit distinct natures, I will introduce a new approach to obtain generalization bounds that combines their strengths.
Thématique(s)
Formation, Recherche - Valorisation
Contact
David Pichardie

Mise à jour le 13 mai 2025