{"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/minimum-regret-search-for-single-and-multi","title":"Minimum Regret Search for Single- and Multi-Task Optimization","arxiv_id":"1602.01064","date":"2016-02-02","proceeding":null,"authors":["Jan Hendrik Metzen"],"abstract":"We propose minimum regret search (MRS), a novel acquisition function for\nBayesian optimization. MRS bears similarities with information-theoretic\napproaches such as entropy search (ES). However, while ES aims in each query at\nmaximizing the information gain with respect to the global maximum, MRS aims at\nminimizing the expected simple regret of its ultimate recommendation for the\noptimum. While empirically ES and MRS perform similar in most of the cases, MRS\nproduces fewer outliers with high simple regret than ES. We provide empirical\nresults both for a synthetic single-task optimization problem as well as for a\nsimulated multi-task robotic control problem.","url_abs":"http://arxiv.org/abs/1602.01064v3","url_pdf":"http://arxiv.org/pdf/1602.01064v3.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":"minimum-regret-search-for-single-and-multi","repo_url":"https://github.com/jmetzen/bayesian_optimization","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"bayesian-optimization","task_name":"Bayesian Optimization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}