arXiv：cs.LG· Gustavo Sutter, Alejandro Comas-Leon, David Holzm\"uller, Hao Wang, Luis Ricardez-Sandoval, Pascal Poupart, Agustinus Kristiadi·· 4 小时前AI 评分33
ELF-BO：利用目标评估延迟实现完全贝叶斯优化
Work While They Sleep: Exploiting Evaluation Latency for Fully Bayesian Optimization
AI 导读
ELF-BO 利用目标函数评估的延迟时间提前采样超参数后验，只需在观测到目标值后重新加权样本，从而在不增加决策时间成本的前提下实现完全贝叶斯优化。在合成函数和真实应用中，ELF-BO 性能与完全贝叶斯方法相当，决策延迟仅与标准贝叶斯优化持平甚至更优，使完全贝叶斯优化在实际场景中变得可行。
正文
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.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML) |
| Cite as: | arXiv:2610.08969 [cs.LG] |
| (or arXiv:2610.08969v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.08969 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Gustavo Sutter [view email]
[v1]
Tue, 6 Oct 2026 18:32:38 UTC (468 KB)
来源：arXiv：cs.LG · arxiv.org