{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/maximizing-acquisition-functions-for-bayesian","title":"Maximizing acquisition functions for Bayesian optimization","arxiv_id":"1805.10196","date":"2018-05-25","proceeding":"NeurIPS 2018 12","authors":["James T. Wilson","Frank Hutter","Marc Peter Deisenroth"],"abstract":"Bayesian optimization is a sample-efficient approach to global optimization\nthat relies on theoretically motivated value heuristics (acquisition functions)\nto guide its search process. Fully maximizing acquisition functions produces\nthe Bayes' decision rule, but this ideal is difficult to achieve since these\nfunctions are frequently non-trivial to optimize. This statement is especially\ntrue when evaluating queries in parallel, where acquisition functions are\nroutinely non-convex, high-dimensional, and intractable. We first show that\nacquisition functions estimated via Monte Carlo integration are consistently\namenable to gradient-based optimization. Subsequently, we identify a common\nfamily of acquisition functions, including EI and UCB, whose properties not\nonly facilitate but justify use of greedy approaches for their maximization.","url_abs":"http://arxiv.org/abs/1805.10196v2","url_pdf":"http://arxiv.org/pdf/1805.10196v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"maximizing-acquisition-functions-for-bayesian","repo_url":"https://github.com/j-wilson/MaximizingAcquisitionFunctions","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"bayesian-optimization","task_name":"Bayesian Optimization"},{"task_slug":"global-optimization","task_name":"global-optimization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1805.10196","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.10196"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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