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Pierre Alquier (ESSEC) - PAC Bayes: introduction and overview
A (condensed) primer on PAC-Bayesian Learning followed by News from the PAC-Bayes frontline
PAC-Bayesian approaches to understanding generalization in deep learning - Gintare Dziugaite
PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees
CS 159 (Spring 2021) -- PAC-Bayesian Theory
MLSS 2021 Taipei- An intro to Statistical Learning Theory and PAC-Bayes Analysis (John Shawe-Taylor)
Part 1: generalization and PAC bayesian learning
Studying Generalization in Deep Learning via PAC-Bayes
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Last Updated: October 14, 2026
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Summary
The goal of machine learning algorithms is to produce predictors having the smallest possible risk (expected loss). Since the ... Speakers: Andrew Foong, David Burt, Javier Antoran Abstract: Benjamin Guedj (2021), A (condensed) primer on Workshop on Theory of Deep Learning: Where next? Topic: Link to the paper: arxiv.org/abs/2002.05551 Paper Authors: Jonas Rothfuss, Vincent Fortuin, Andreas Krause Abstract: ... Slides: 1five9.github.io/slides/learning/11.pdf. MLSS 2021 Taipei online course Time: 8/9(Mon) 20:00-23:00 Speaker: John Shawe-Taylor Title: An introduction to Statistical ... Gintare Karolina Dziugaite (Element AI) simons.berkeley.edu/talks/tbd-77 Frontiers of Deep Learning.