{"type":"ScholarlyArticle","title":"Bayesian Optimization using Pseudo-Points","authors":[{"name":"Chao Qian","slug":"chao-qian","given":"Chao","family":"Qian","affiliation":"National Key Laboratory for Novel Software Technology , Nanjing University , Nanjing 210023 , China"},{"name":"Hang Xiong","slug":"hang-xiong","given":"Hang","family":"Xiong","affiliation":"University of Science and Technology of China , Hefei 230027 , China"},{"name":"Ke Xue","slug":"ke-xue","given":"Ke","family":"Xue","affiliation":"National Key Laboratory for Novel Software Technology , Nanjing University , Nanjing 210023 , China"}],"abstract":"Bayesian optimization (BO) is a popular approach for expensive black-box optimization, with applications including parameter tuning, experimental design, and robotics. BO usually models the objective function by a Gaussian process (GP), and iteratively samples the next data point by maximizing an acquisition function. In this paper, we propose a new general framework for BO by generating pseudo-points (i.e., data points whose objective values are not evaluated) to improve the GP model. With the classic acquisition function, i.e., upper confidence bound (UCB), we prove that the cumulative regret can be generally upper bounded. Experiments using UCB and other acquisition functions, i.e., probability of improvement (PI) and expectation of improvement (EI), on synthetic as well as real-world problems clearly show the advantage of generating pseudo-points.","doi":"10.24963/ijcai.2020/421","url":"https://ijcai.org/proceedings/2020/421","pdf":"https://ijcai.org/proceedings/2020/0421.pdf","markdown":"https://ijcai.org/proceedings/2020/421.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/421","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":421,"pages":{"first":3044,"last":3050},"keywords":["Machine Learning: Bayesian Optimization","Heuristic Search and Game Playing: Heuristic Search","Heuristic Search and Game Playing: Heuristic Search and Machine Learning"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24911/file_24911.m3u8","length_sec":290,"provider":"hls"}],"references":[{"position":1,"raw":"[Azimi et al., 2010] J. 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