Near Optimal Non-asymptotic Sample Complexity of 1-Identification
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866916909681737728 |
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| author | Li, Zitian Cheung, Wang Chi |
| author_facet | Li, Zitian Cheung, Wang Chi |
| contents | Motivated by an open direction in existing literature, we study the 1-identification problem, a fundamental multi-armed bandit formulation on pure exploration. The goal is to determine whether there exists an arm whose mean reward is at least a known threshold $μ_0$, or to output None if it believes such an arm does not exist. The agent needs to guarantee its output is correct with probability at least $1-δ$. Degenne & Koolen 2019 has established the asymptotically tight sample complexity for the 1-identification problem, but they commented that the non-asymptotic analysis remains unclear. We design a new algorithm Sequential-Exploration-Exploitation (SEE), and conduct theoretical analysis from the non-asymptotic perspective. Novel to the literature, we achieve near optimality, in the sense of matching upper and lower bounds on the pulling complexity. The gap between the upper and lower bounds is up to a polynomial logarithmic factor. The numerical result also indicates the effectiveness of our algorithm, compared to existing benchmarks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_06978 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Near Optimal Non-asymptotic Sample Complexity of 1-Identification Li, Zitian Cheung, Wang Chi Machine Learning Motivated by an open direction in existing literature, we study the 1-identification problem, a fundamental multi-armed bandit formulation on pure exploration. The goal is to determine whether there exists an arm whose mean reward is at least a known threshold $μ_0$, or to output None if it believes such an arm does not exist. The agent needs to guarantee its output is correct with probability at least $1-δ$. Degenne & Koolen 2019 has established the asymptotically tight sample complexity for the 1-identification problem, but they commented that the non-asymptotic analysis remains unclear. We design a new algorithm Sequential-Exploration-Exploitation (SEE), and conduct theoretical analysis from the non-asymptotic perspective. Novel to the literature, we achieve near optimality, in the sense of matching upper and lower bounds on the pulling complexity. The gap between the upper and lower bounds is up to a polynomial logarithmic factor. The numerical result also indicates the effectiveness of our algorithm, compared to existing benchmarks. |
| title | Near Optimal Non-asymptotic Sample Complexity of 1-Identification |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2506.06978 |