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A (condensed) primer on PAC-Bayesian learning, followed by News from the PAC-Bayes frontline
CS 159 (Spring 2021) -- PAC-Bayesian Theory
NIPS 2016 spotlight - PAC Bayesian Theory Meets Bayesian Inference
PAC-Bayesian Machine Learning: Learning by Optimizing a Performance Guarantee
From Flat Minima to Numerically Nonvacuous Generalization Bounds via PAC-Bayes (Talk)
A (condensed) primer on PAC-Bayesian Learning followed by News from the PAC-Bayes frontline
PAC Bayesian Learning and Domain Adaptation
Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks
AISTATS 2023: PAC-Bayesian Learning of Optimization Algorithms
A primer on PAC-Bayesian learning, and application to deep neural networks
PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees
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Last Updated: October 14, 2026
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NIPS 2017 workshop "(Almost) 50 Shades of Bayesian Learning: Slides: 1five9.github.io/slides/learning/11.pdf. NIPS 2016 spotlight Poster (Mon Dec 5th) Manuscript: arxiv.org/abs/1605.08636 Slides: ... The goal of machine learning algorithms is to produce predictors having the smallest possible risk (expected loss). Since the ... Benjamin Guedj (2021), A (condensed) primer on Quick overview of our 2019 NeurIPS paper about studying Deep Neural Networks with binary activations using the In this video, I give a short introduction into our current research paper " Seminar by Benjamin Guedj at the UCL Centre for AI. Recorded on the 16th June 2020. Abstract: Link to the paper: arxiv.org/abs/2002.05551 Paper Authors: Jonas Rothfuss, Vincent Fortuin, Andreas Krause Abstract: ...
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