{"type":"ScholarlyArticle","title":"Randomised Gaussian Process Upper Confidence Bound for Bayesian Optimisation","authors":[{"name":"Julian Berk","slug":"julian-berk","given":"Julian","family":"Berk","affiliation":"Applied Artificial Intelligence Institute"},{"name":"Sunil Gupta","slug":"sunil-gupta","given":"Sunil","family":"Gupta","affiliation":"Applied Artificial Intelligence Institute"},{"name":"Santu Rana","slug":"santu-rana","given":"Santu","family":"Rana","affiliation":"Applied Artificial Intelligence Institute"},{"name":"Svetha Venkatesh","slug":"svetha-venkatesh","given":"Svetha","family":"Venkatesh","affiliation":"Applied Artificial Intelligence Institute"}],"abstract":"In order to improve the performance of Bayesian optimisation, we develop a modified Gaussian process upper confidence bound (GP-UCB) acquisition function. This is done by sampling the exploration-exploitation trade-off parameter from a distribution. We prove that this allows the expected trade-off parameter to be altered to better suit the problem without compromising a bound on the function's Bayesian regret. We also provide results showing that our method achieves better performance than GP-UCB in a range of real-world and synthetic problems.","doi":"10.24963/ijcai.2020/316","url":"https://ijcai.org/proceedings/2020/316","pdf":"https://ijcai.org/proceedings/2020/0316.pdf","markdown":"https://ijcai.org/proceedings/2020/316.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/316","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Machine Learning","track":"Main track","number":316,"pages":{"first":2284,"last":2290},"keywords":["Machine Learning: Cost-Sensitive Learning","Machine Learning: Bayesian Optimization"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24494/file_24494.m3u8","length_sec":284,"provider":"hls"}],"references":[{"position":1,"raw":"[Brochu et al., 2010] Eric Brochu, Vlad M Cora, and Nando De Freitas. 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