Gap Entropy and Almost Instance-Wise Optimal Best-Arm Identification
In the best-arm identification problem, we are given $n$ stochastic arms with unknown means and wish to identify the arm with the largest mean with probability at least $1-δ$, using as few samples as possible. We consider independent Gaussian rewards with unit variance and means in $[0,1]$. Chen and Li [2016] conjectured that the instance-wise sample complexity of this problem is characterized by the gap entropy, up to an additive term arising from the two-arm problem. In this paper, we resolve their gap-entropy and almost instance-wise optimality conjectures. For an instance $I$, let $Δ_{[i]}$ be the gap between the largest and the $i$-th largest mean, let $H(I)=\sum_{i=2}^{n}Δ_{[i]}^{-2...
| Authors | Jiarui Yao, Jiaxi Zhao, Xiangxin Zhou |
|---|---|
| Primary category | cs.CC |
| Published | 12 September 2026 |
| Last updated at source | 12 September 2026 |
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