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 estimate. Alternatively, a fully Bayesian approach uses model averaging to account for uncertainty over the hyperparameters, leading to better uncertainty estimates---useful in the low-data regime that is pervasive in BO. However, it is often prohibitively expensive and thus rarely used. In this work, we propose ELF-BO, an algorithm that uses the objective evaluation latency to headstart the computation of the next suggestion, allowing for fully Bayesian optimization without incurring substantial decision-time costs. This is done by sampling from the hyperparameter posterior \emph{while} the objective is being evaluated, only requiring reweighting of the samples once the objective value is observed. Across synthetic functions and real-world applications, we show that ELF-BO matches the performance of fully Bayesian methods while only incurring decision latency on par with or better than standard BO. Thus, ELF-BO makes fully Bayesian optimization practical in real-world use cases.
Keywords
Publication details
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Cite this article
APA 7
Sutter, G., Comas-Leon, A., Holzmüller, D., Wang, H., Ricardez-Sandoval, L., Poupart, P., & Kristiadi, A. (2026). Work While They Sleep: Exploiting Evaluation Latency for Fully Bayesian Optimization. https://omanscience.com/en/articles/work-while-they-sleep-exploiting-evaluation-latency-for-fully-bayesian-optimization
MLA 9
Sutter, Gustavo, et al. "Work While They Sleep: Exploiting Evaluation Latency for Fully Bayesian Optimization." https://omanscience.com/en/articles/work-while-they-sleep-exploiting-evaluation-latency-for-fully-bayesian-optimization.
Chicago (author–date)
Sutter, Gustavo, Alejandro Comas-Leon, David Holzmüller, Hao Wang, Luis Ricardez-Sandoval, Pascal Poupart, and Agustinus Kristiadi. 2026. "Work While They Sleep: Exploiting Evaluation Latency for Fully Bayesian Optimization." https://omanscience.com/en/articles/work-while-they-sleep-exploiting-evaluation-latency-for-fully-bayesian-optimization.
Harvard
Sutter, G., Comas-Leon, A., Holzmüller, D., Wang, H., Ricardez-Sandoval, L., Poupart, P. and Kristiadi, A. (2026) 'Work While They Sleep: Exploiting Evaluation Latency for Fully Bayesian Optimization', Available at: https://omanscience.com/en/articles/work-while-they-sleep-exploiting-evaluation-latency-for-fully-bayesian-optimization.
Vancouver
Sutter G, Comas-Leon A, Holzmüller D, Wang H, Ricardez-Sandoval L, Poupart P, et al. Work While They Sleep: Exploiting Evaluation Latency for Fully Bayesian Optimization. https://omanscience.com/en/articles/work-while-they-sleep-exploiting-evaluation-latency-for-fully-bayesian-optimization
IEEE
G. Sutter, A. Comas-Leon, D. Holzmüller, H. Wang, L. Ricardez-Sandoval, P. Poupart, and A. Kristiadi, "Work While They Sleep: Exploiting Evaluation Latency for Fully Bayesian Optimization," https://omanscience.com/en/articles/work-while-they-sleep-exploiting-evaluation-latency-for-fully-bayesian-optimization.