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September 29, 2025Open Access

How good is PAC-Bayes at explaining generalisation?

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The optimal generalisation guarantee hinges on the prior distribution's risk profile, ensuring effectiveness.
Achieving a desired generalisation level necessitates placing weight on high-performing predictors within the prior.
Using data-dependent priors in deep learning applications directly influences PAC-Bayes' capacity to explain generalisation.
Understanding these conditions raises critical discussions about PAC-Bayes claims in machine learning contexts.

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Analysis reveals PAC-Bayes generalisation guarantees depend on prior distribution choice, indicating essential mass on top predictors.

Picard-Weibel et al. (2025) studied this question.

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