AI RESEARCH
Optimal-Point Variance Reduction For Bayesian Optimization With Regret Guarantee
arXiv CS.LG
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ArXi:2606.00956v1 Announce Type: new This paper studies a one-step lookahead Bayesian optimization (BO) method and its theoretical guarantee. Although the empirical effectiveness of one-step lookahead BO methods, such as entropy search, has been studied extensively, they often rely on computationally intractable approximations, and their regret guarantees remain underdeveloped. Thus, this paper proposes a one-step lookahead BO method called optimal-point variance reduction (OVR), which requires only posterior sampling and Monte Carlo approximations.