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| Главные авторы: | , |
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| Формат: | Preprint |
| Опубликовано: |
2025
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| Предметы: | |
| Online-ссылка: | https://arxiv.org/abs/2501.06339 |
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| _version_ | 1866909454376632320 |
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| author | Nguyen-Tang, Thanh Arora, Raman |
| author_facet | Nguyen-Tang, Thanh Arora, Raman |
| contents | We study the statistical complexity of offline decision-making with function approximation, establishing (near) minimax-optimal rates for stochastic contextual bandits and Markov decision processes. The performance limits are captured by the pseudo-dimension of the (value) function class and a new characterization of the behavior policy that \emph{strictly} subsumes all the previous notions of data coverage in the offline decision-making literature. In addition, we seek to understand the benefits of using offline data in online decision-making and show nearly minimax-optimal rates in a wide range of regimes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_06339 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | On The Statistical Complexity of Offline Decision-Making Nguyen-Tang, Thanh Arora, Raman Machine Learning Artificial Intelligence We study the statistical complexity of offline decision-making with function approximation, establishing (near) minimax-optimal rates for stochastic contextual bandits and Markov decision processes. The performance limits are captured by the pseudo-dimension of the (value) function class and a new characterization of the behavior policy that \emph{strictly} subsumes all the previous notions of data coverage in the offline decision-making literature. In addition, we seek to understand the benefits of using offline data in online decision-making and show nearly minimax-optimal rates in a wide range of regimes. |
| title | On The Statistical Complexity of Offline Decision-Making |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2501.06339 |