{
  "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.08969",
  "author_total": 7,
  "authors": [
    {
      "name": "Gustavo Sutter"
    },
    {
      "name": "Alejandro Comas-Leon"
    },
    {
      "name": "David Holzmüller"
    },
    {
      "name": "Hao Wang"
    },
    {
      "name": "Luis Ricardez-Sandoval"
    },
    {
      "name": "Pascal Poupart"
    },
    {
      "name": "Agustinus Kristiadi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3qdvbynxzq0az.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.08969",
  "fulltext_url": null,
  "id": "3qdvbynxzq0az",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.08969",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.AI"
    ],
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "stat.ML"
    ],
    [
      "openalex",
      "1803|Advanced Bandit Algorithms Research"
    ]
  ],
  "openalex": "W7221045472",
  "pdf_url": "https://arxiv.org/pdf/2610.08969",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-08",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3qdvbynxzq0az.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:28:46Z",
      "native_id": "2610.08969",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.08969"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ML",
      "subfield": "statistics"
    }
  ],
  "title": "Work While They Sleep: Exploiting Evaluation Latency for Fully Bayesian Optimization",
  "title_text": "Work While They Sleep: Exploiting Evaluation Latency for Fully Bayesian Optimization",
  "type": "preprint",
  "updated_at": "2026-10-30T12:28:46Z",
  "url": "https://research.draconitic.com/paper/3qdvbynxzq0az",
  "venue": "arXiv",
  "versions": []
}