arXiv Machine Learning
BONSAI: Bayesian Optimization with Natural Simplicity and Interpretability
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Pre-training of Bayesian Optimization Algorithm through Bayesian Optimization
arXiv:2610.10186v1 Announce Type: new Abstract: Bayesian optimization (BO) is widely used as a standard approach for expensive black-box optimization. However, BO algorithms often involve parameters...
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Robust Bayesian Optimization via Tempered Posteriors
arXiv:2601. 07094v2 Announce Type: replace-cross Abstract: Bayesian optimization (BO) iteratively fits a Gaussian process (GP) surrogate to accumulated evaluations and selects new queries via an acquisition function.
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Work While They Sleep: Exploiting Evaluation Latency for Fully Bayesian Optimization
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Towards Regret Guarantees for One-Step Lookahead Bayesian Optimization
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.