arXiv — cs.AI preprintsInternational12 October 2026
Work While They Sleep: Exploiting Evaluation Latency for Fully Bayesian Optimization
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arXiv:2610.08969v2 Announce Type: replace-cross Abstract: Black-box optimization problems are ubiquitous across science and engineering, often dealing with expensive objective functions. This objective latency has two consequences during optimization: (i) the objective evaluation dominates execution time, and (ii) sample-efficient algorithms are crucial to accelerate development and avoid wasting resources. Bayesian optimization (BO) methods are the \textit{de facto} choice of planners for suggesting the next point to try. Standard BO fits the surrogate model's hyperparameters with a point est
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