Junpei Komiyama

A simple, fixed-budget best-arm identification algorithm can be made nearly as adaptive as state-of-the-art methods by optimizing its phase schedule, achieving $11/12$ of the optimal normalized rate for Gaussian arms.

A linear bandit algorithm can slash action selection costs by 99% by only evaluating a small, persistent subset of candidates plus a few challengers, without sacrificing regret.