Multi-View Majority Vote Learning Algorithms: Direct Minimization of PAC-Bayesian Bounds
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911207865188352 |
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| author | Hennequin, Mehdi Zitouni, Abdelkrim Benabdeslem, Khalid Elghazel, Haytham Gaci, Yacine |
| author_facet | Hennequin, Mehdi Zitouni, Abdelkrim Benabdeslem, Khalid Elghazel, Haytham Gaci, Yacine |
| contents | The PAC-Bayesian framework has significantly advanced the understanding of statistical learning, particularly for majority voting methods. Despite its successes, its application to multi-view learning -- a setting with multiple complementary data representations -- remains underexplored. In this work, we extend PAC-Bayesian theory to multi-view learning, introducing novel generalization bounds based on Rényi divergence. These bounds provide an alternative to traditional Kullback-Leibler divergence-based counterparts, leveraging the flexibility of Rényi divergence. Furthermore, we propose first- and second-order oracle PAC-Bayesian bounds and extend the C-bound to multi-view settings. To bridge theory and practice, we design efficient self-bounding optimization algorithms that align with our theoretical results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_06276 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-View Majority Vote Learning Algorithms: Direct Minimization of PAC-Bayesian Bounds Hennequin, Mehdi Zitouni, Abdelkrim Benabdeslem, Khalid Elghazel, Haytham Gaci, Yacine Machine Learning Artificial Intelligence The PAC-Bayesian framework has significantly advanced the understanding of statistical learning, particularly for majority voting methods. Despite its successes, its application to multi-view learning -- a setting with multiple complementary data representations -- remains underexplored. In this work, we extend PAC-Bayesian theory to multi-view learning, introducing novel generalization bounds based on Rényi divergence. These bounds provide an alternative to traditional Kullback-Leibler divergence-based counterparts, leveraging the flexibility of Rényi divergence. Furthermore, we propose first- and second-order oracle PAC-Bayesian bounds and extend the C-bound to multi-view settings. To bridge theory and practice, we design efficient self-bounding optimization algorithms that align with our theoretical results. |
| title | Multi-View Majority Vote Learning Algorithms: Direct Minimization of PAC-Bayesian Bounds |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2411.06276 |