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best-arm-identification

topic2 events
papersTODAY 04:00 UTC

New Gap Entropy Method Nears Instance-Wise Optimal Best-Arm Identification

Researchers introduce a quantity called gap entropy for the best-arm identification problem with independent Gaussian arms, where the goal is to find the highest-mean arm using as few samples as possible at a given confidence level. They show that an algorithm based on this measure comes close to the optimal sample complexity for each individual problem instance. The work is a theoretical contribution posted to arXiv and has not yet been peer reviewed.

arxiv/bandits/sample-complexity/theory
papersSEP 10 04:00 UTC

Researchers prove gap-entropy conjecture for fixed-confidence best-arm identification

A new arXiv paper in machine learning theory settles the gap-entropy conjecture, an open problem in best-arm identification for multi-armed bandits. The proof covers the fixed-confidence setting with independent unit-variance Gaussian arms, means bounded in [0,1], and a single optimal arm. The result confirms that the entropy of suboptimality gaps governs the sample complexity needed to identify the best arm.

bandits/theory/sample-complexity/best-arm-identification