Near Optimal Pure Exploration in Logistic Bandits
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917915946647552 |
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| author | Rivera, Eduardo Ochoa Tewari, Ambuj |
| author_facet | Rivera, Eduardo Ochoa Tewari, Ambuj |
| contents | Bandit algorithms have garnered significant attention due to their practical applications in real-world scenarios. However, beyond simple settings such as multi-arm or linear bandits, optimal algorithms remain scarce. Notably, no optimal solution exists for pure exploration problems in the context of generalized linear model (GLM) bandits. In this paper, we narrow this gap and develop the first track-and-stop algorithm for general pure exploration problems under the logistic bandit called logistic track-and-stop (Log-TS). Log-TS is an efficient algorithm that asymptotically matches an approximation for the instance-specific lower bound of the expected sample complexity up to a logarithmic factor. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_20640 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Near Optimal Pure Exploration in Logistic Bandits Rivera, Eduardo Ochoa Tewari, Ambuj Machine Learning Bandit algorithms have garnered significant attention due to their practical applications in real-world scenarios. However, beyond simple settings such as multi-arm or linear bandits, optimal algorithms remain scarce. Notably, no optimal solution exists for pure exploration problems in the context of generalized linear model (GLM) bandits. In this paper, we narrow this gap and develop the first track-and-stop algorithm for general pure exploration problems under the logistic bandit called logistic track-and-stop (Log-TS). Log-TS is an efficient algorithm that asymptotically matches an approximation for the instance-specific lower bound of the expected sample complexity up to a logarithmic factor. |
| title | Near Optimal Pure Exploration in Logistic Bandits |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2410.20640 |