Rethinking Successive Rejects Algorithms
In the best-arm identification problem, an algorithm adaptively selects arms to find the arm of the largest mean reward. Successive Rejects (SR) is a simple fixed-budget best-arm identification algorithm that eliminates one candidate arm at each phase. At first glance, SR seems to have limited adaptivity, since it follows predetermined phase lengths. However, for Gaussian arms with common variance, SR's worst-instance error exponent normalized by the popular complexity measure $H_1(P)$ is optimal up to a constant factor, which partly explains why SR's performance is competitive compared with state-of-the-art adaptive sampling algorithms. In this paper, we extend the framework of SR by opt...
| Authors | Mohamed Khair, Junpei Komiyama |
|---|---|
| Primary category | stat.ML |
| Published | 15 October 2026 |
| Last updated at source | 15 October 2026 |
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