{"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/batched-large-scale-bayesian-optimization-in","title":"Batched Large-scale Bayesian Optimization in High-dimensional Spaces","arxiv_id":"1706.01445","date":"2017-06-05","proceeding":null,"authors":["Zi Wang","Clement Gehring","Pushmeet Kohli","Stefanie Jegelka"],"abstract":"Bayesian optimization (BO) has become an effective approach for black-box\nfunction optimization problems when function evaluations are expensive and the\noptimum can be achieved within a relatively small number of queries. However,\nmany cases, such as the ones with high-dimensional inputs, may require a much\nlarger number of observations for optimization. Despite an abundance of\nobservations thanks to parallel experiments, current BO techniques have been\nlimited to merely a few thousand observations. In this paper, we propose\nensemble Bayesian optimization (EBO) to address three current challenges in BO\nsimultaneously: (1) large-scale observations; (2) high dimensional input\nspaces; and (3) selections of batch queries that balance quality and diversity.\nThe key idea of EBO is to operate on an ensemble of additive Gaussian process\nmodels, each of which possesses a randomized strategy to divide and conquer. We\nshow unprecedented, previously impossible results of scaling up BO to tens of\nthousands of observations within minutes of computation.","url_abs":"http://arxiv.org/abs/1706.01445v4","url_pdf":"http://arxiv.org/pdf/1706.01445v4.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":"batched-large-scale-bayesian-optimization-in","repo_url":"https://github.com/SK-tklab/RandomFourierFeatures","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"batched-large-scale-bayesian-optimization-in","repo_url":"https://github.com/zi-w/Ensemble-Bayesian-Optimization","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"bayesian-optimization","task_name":"Bayesian Optimization"},{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"high","task_name":"Vocal Bursts Intensity Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1706.01445","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}