{"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-in-a-billion-dimensions","title":"Bayesian Optimization in a Billion Dimensions via Random Embeddings","arxiv_id":"1301.1942","date":"2013-01-09","proceeding":null,"authors":["Ziyu Wang","Frank Hutter","Masrour Zoghi","David Matheson","Nando de Freitas"],"abstract":"Bayesian optimization techniques have been successfully applied to robotics,\nplanning, sensor placement, recommendation, advertising, intelligent user\ninterfaces and automatic algorithm configuration. Despite these successes, the\napproach is restricted to problems of moderate dimension, and several workshops\non Bayesian optimization have identified its scaling to high-dimensions as one\nof the holy grails of the field. In this paper, we introduce a novel random\nembedding idea to attack this problem. The resulting Random EMbedding Bayesian\nOptimization (REMBO) algorithm is very simple, has important invariance\nproperties, and applies to domains with both categorical and continuous\nvariables. We present a thorough theoretical analysis of REMBO. Empirical\nresults confirm that REMBO can effectively solve problems with billions of\ndimensions, provided the intrinsic dimensionality is low. They also show that\nREMBO achieves state-of-the-art performance in optimizing the 47 discrete\nparameters of a popular mixed integer linear programming solver.","url_abs":"http://arxiv.org/abs/1301.1942v2","url_pdf":"http://arxiv.org/pdf/1301.1942v2.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-in-a-billion-dimensions","repo_url":"https://github.com/ziyuw/rembo","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"bayesian-optimization","task_name":"Bayesian Optimization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1301.1942","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}