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A Primer on PAC-Bayesian Learning

2019/01/31 by Benjamin Guedj · 2 citations
Mathematics · Computer Science · #stat.ML #cs.LG

paper · pdf

published as Proceedings of the 2nd congress of the Société Mathématique de France, 2019, pp. 391--414

arxiv created 2019/05/07 · arxiv updated 2020/02/06

Abstract

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.

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