Towards Regret Guarantees for One-Step Lookahead Bayesian Optimization
This paper studies theoretical guarantees of a one-step lookahead Bayesian optimization (BO) method. 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 analyzes a one-step lookahead BO method, which we refer to as optimal-point variance reduction (OVR), that requires only posterior sampling and Monte Carlo approximations. We obtain a uniform Monte Carlo estimation error bound over an input domain in an acquisition function computation. Furthermore, we show that the regularized OVR, with ...
| Authors | Shion Takeno |
|---|---|
| Primary category | cs.LG |
| Published | 31 May 2026 |
| Last updated at source | 7 October 2026 |
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