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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.08231 |
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| _version_ | 1866915191231348736 |
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| author | Picard-Weibel, Antoine Clerico, Eugenio Moscoviz, Roman Guedj, Benjamin |
| author_facet | Picard-Weibel, Antoine Clerico, Eugenio Moscoviz, Roman Guedj, Benjamin |
| contents | We discuss necessary conditions for a PAC-Bayes bound to provide a meaningful generalisation guarantee. Our analysis reveals that the optimal generalisation guarantee depends solely on the distribution of the risk induced by the prior distribution. In particular, achieving a target generalisation level is only achievable if the prior places sufficient mass on high-performing predictors. We relate these requirements to the prevalent practice of using data-dependent priors in deep learning PAC-Bayes applications, and discuss the implications for the claim that PAC-Bayes ``explains'' generalisation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_08231 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | How good is PAC-Bayes at explaining generalisation? Picard-Weibel, Antoine Clerico, Eugenio Moscoviz, Roman Guedj, Benjamin Machine Learning We discuss necessary conditions for a PAC-Bayes bound to provide a meaningful generalisation guarantee. Our analysis reveals that the optimal generalisation guarantee depends solely on the distribution of the risk induced by the prior distribution. In particular, achieving a target generalisation level is only achievable if the prior places sufficient mass on high-performing predictors. We relate these requirements to the prevalent practice of using data-dependent priors in deep learning PAC-Bayes applications, and discuss the implications for the claim that PAC-Bayes ``explains'' generalisation. |
| title | How good is PAC-Bayes at explaining generalisation? |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2503.08231 |