Team, Visitors, External Collaborators
Overall Objectives
Research Program
Application Domains
Highlights of the Year
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
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Section: New Results

Axis 2: A Primer on PAC-Bayesian Learning

Participant: Benjamin Guedj

This survey on PAC-Bayesian learning has been the backbone to a successful proposal for an ICML 2019 plenary tutorial.

Generalised Bayesian learning algorithms are increasingly popular in machine learning, due to their PAC generalisation properties and flexibility. The present paper aims at providing a self-contained survey on the resulting PAC-Bayes framework and some of its main theoretical and algorithmic developments.

This work has been published in the proceedings of the French Mathematical Society. Published as [66].