arXiv Machine Learning October 8, 2026 By Shion Takeno

Towards Regret Guarantees for One-Step Lookahead Bayesian Optimization

Read the original on arXiv Machine Learning →

The paper investigates theoretical guarantees for a one‑step lookahead Bayesian optimization method called optimal‑point variance reduction (OVR). It derives a uniform Monte Carlo estimation error bound for the acquisition function and shows that a regularized version of OVR achieves a vanishing Bayesian expected simple regret bound. Numerical experiments validate the performance of both OVR and its regularized variant.

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