{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T06:40:55Z","timestamp":1784097655205,"version":"3.55.0"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,7]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.24963\/ijcai.2020\/316","type":"proceedings-article","created":{"date-parts":[[2020,7,8]],"date-time":"2020-07-08T12:12:10Z","timestamp":1594210330000},"page":"2284-2290","source":"Crossref","is-referenced-by-count":20,"title":["Randomised Gaussian Process Upper Confidence Bound for Bayesian Optimisation"],"prefix":"10.24963","author":[{"given":"Julian","family":"Berk","sequence":"first","affiliation":[{"name":"Deakin University, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sunil","family":"Gupta","sequence":"additional","affiliation":[{"name":"Deakin University, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Santu","family":"Rana","sequence":"additional","affiliation":[{"name":"Deakin University, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Svetha","family":"Venkatesh","sequence":"additional","affiliation":[{"name":"Deakin University, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"10584","event":{"name":"Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}","theme":"Artificial Intelligence","location":"Yokohama, Japan","acronym":"IJCAI-PRICAI-2020","number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2020,7,11]]},"end":{"date-parts":[[2020,7,17]]}},"container-title":["Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2020,7,9]],"date-time":"2020-07-09T02:14:27Z","timestamp":1594260867000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2020\/316"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2020,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2020\/316","relation":{},"subject":[],"published":{"date-parts":[[2020,7]]}}}