{"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/bayesian-optimization-of-combinatorial","title":"Bayesian Optimization of Combinatorial Structures","arxiv_id":"1806.08838","date":"2018-06-22","proceeding":"ICML 2018 7","authors":["Ricardo Baptista","Matthias Poloczek"],"abstract":"The optimization of expensive-to-evaluate black-box functions over\ncombinatorial structures is an ubiquitous task in machine learning, engineering\nand the natural sciences. The combinatorial explosion of the search space and\ncostly evaluations pose challenges for current techniques in discrete\noptimization and machine learning, and critically require new algorithmic\nideas. This article proposes, to the best of our knowledge, the first algorithm\nto overcome these challenges, based on an adaptive, scalable model that\nidentifies useful combinatorial structure even when data is scarce. Our\nacquisition function pioneers the use of semidefinite programming to achieve\nefficiency and scalability. Experimental evaluations demonstrate that this\nalgorithm consistently outperforms other methods from combinatorial and\nBayesian optimization.","url_abs":"http://arxiv.org/abs/1806.08838v2","url_pdf":"http://arxiv.org/pdf/1806.08838v2.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":"bayesian-optimization-of-combinatorial","repo_url":"https://github.com/baptistar/BOCS","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"GPL-3.0"}},{"paper_slug":"bayesian-optimization-of-combinatorial","repo_url":"https://github.com/aryandeshwal/Submodular_Relaxation_BOCS","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"bayesian-optimization","task_name":"Bayesian Optimization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1806.08838","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}