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A general framework for the practical disintegration of PAC-Bayesian bounds.

, , , and . Mach. Learn., 113 (2): 519-604 (February 2024)

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Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound., , , , , , and . NeurIPS, page 455-467. (2021)A PAC-Bayes Analysis of Adversarial Robustness., , , and . NeurIPS, page 14421-14433. (2021)Uniform Generalization Bounds on Data-Dependent Hypothesis Sets via PAC-Bayesian Theory on Random Sets., , , and . CoRR, (2024)A general framework for the practical disintegration of PAC-Bayesian bounds., , , and . Mach. Learn., 113 (2): 519-604 (February 2024)Self-bounding Majority Vote Learning Algorithms by the Direct Minimization of a Tight PAC-Bayesian C-Bound., , , and . ECML/PKDD (2), volume 12976 of Lecture Notes in Computer Science, page 167-183. Springer, (2021)Leveraging PAC-Bayes Theory and Gibbs Distributions for Generalization Bounds with Complexity Measures., , , , and . AISTATS, volume 238 of Proceedings of Machine Learning Research, page 3007-3015. PMLR, (2024)PAC-Bayesian Bounds and Beyond: Self-Bounding Algorithms and New Perspectives on Generalization in Machine Learning. (Bornes PAC-Bayésiennes et Au-delà: Algorithmes Auto-limitatifs et Nouvelles Perspectives sur la Généralisation en Apprentissage Automatique).. Jean Monnet University, Saint-Étienne, France, (2022)A Theoretically Grounded Extension of Universal Attacks from the Attacker's Viewpoint., , , , , and . ECML/PKDD (4), volume 14944 of Lecture Notes in Computer Science, page 283-300. Springer, (2024)