{"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/fast-information-theoretic-bayesian","title":"Fast Information-theoretic Bayesian Optimisation","arxiv_id":"1711.00673","date":"2017-11-02","proceeding":"ICML 2018 7","authors":["Binxin Ru","Mark McLeod","Diego Granziol","Michael A. Osborne"],"abstract":"Information-theoretic Bayesian optimisation techniques have demonstrated\nstate-of-the-art performance in tackling important global optimisation\nproblems. However, current information-theoretic approaches require many\napproximations in implementation, introduce often-prohibitive computational\noverhead and limit the choice of kernels available to model the objective. We\ndevelop a fast information-theoretic Bayesian Optimisation method, FITBO, that\navoids the need for sampling the global minimiser, thus significantly reducing\ncomputational overhead. Moreover, in comparison with existing approaches, our\nmethod faces fewer constraints on kernel choice and enjoys the merits of\ndealing with the output space. We demonstrate empirically that FITBO inherits\nthe performance associated with information-theoretic Bayesian optimisation,\nwhile being even faster than simpler Bayesian optimisation approaches, such as\nExpected Improvement.","url_abs":"http://arxiv.org/abs/1711.00673v5","url_pdf":"http://arxiv.org/pdf/1711.00673v5.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":"fast-information-theoretic-bayesian","repo_url":"https://github.com/rubinxin/FITBO","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"bayesian-optimisation","task_name":"Bayesian Optimisation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}