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| Main Author: | |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.14469 |
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| _version_ | 1866929478435864576 |
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| author | Maurer, Andreas |
| author_facet | Maurer, Andreas |
| contents | The paper proves generalization results for a class of stochastic learning algorithms. The method applies whenever the algorithm generates an absolutely continuous distribution relative to some a-priori measure and the Radon Nikodym derivative has subgaussian concentration. Applications are bounds for the Gibbs algorithm and randomizations of stable deterministic algorithms as well as PAC-Bayesian bounds with data-dependent priors. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_14469 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Generalization of Hamiltonian algorithms Maurer, Andreas Machine Learning The paper proves generalization results for a class of stochastic learning algorithms. The method applies whenever the algorithm generates an absolutely continuous distribution relative to some a-priori measure and the Radon Nikodym derivative has subgaussian concentration. Applications are bounds for the Gibbs algorithm and randomizations of stable deterministic algorithms as well as PAC-Bayesian bounds with data-dependent priors. |
| title | Generalization of Hamiltonian algorithms |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2405.14469 |