[
    {
        "id": "osp-15467",
        "type": "article-journal",
        "title": "Work While They Sleep: Exploiting Evaluation Latency for Fully Bayesian Optimization",
        "author": [
            {
                "family": "Sutter",
                "given": "Gustavo"
            },
            {
                "family": "Comas-Leon",
                "given": "Alejandro"
            },
            {
                "family": "Holzmüller",
                "given": "David"
            },
            {
                "family": "Wang",
                "given": "Hao"
            },
            {
                "family": "Ricardez-Sandoval",
                "given": "Luis"
            },
            {
                "family": "Poupart",
                "given": "Pascal"
            },
            {
                "family": "Kristiadi",
                "given": "Agustinus"
            }
        ],
        "URL": "https://omanscience.com/en/articles/work-while-they-sleep-exploiting-evaluation-latency-for-fully-bayesian-optimization",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "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."
    }
]