AI RESEARCH
Nearly Optimal Best Arm Identification for Semiparametric Bandits
arXiv CS.LG
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ArXi:2604.03969v1 Announce Type: cross We study fixed-confidence Best Arm Identification (BAI) in semiparametric bandits, where rewards are linear in arm features plus an unknown additive baseline shift. Unlike linear-bandit BAI, this setting requires orthogonalized regression, and its instance-optimal sample complexity has remained open. For the transductive setting, we establish an attainable instance-dependent lower bound characterized by the corresponding linear-bandit complexity on shifted features.