{"type":"ScholarlyArticle","title":"Bayesian Nonparametric Space Partitions: A Survey","authors":[{"name":"Xuhui Fan","slug":"xuhui-fan","given":"Xuhui","family":"Fan","affiliation":""},{"name":"Bin Li","slug":"bin-li","given":"Bin","family":"Li","affiliation":""},{"name":"Ling Luo","slug":"ling-luo","given":"Ling","family":"Luo","affiliation":""},{"name":"Scott A. Sisson","slug":"scott-a-sisson","given":"Scott A.","family":"Sisson","affiliation":""}],"abstract":"Bayesian nonparametric space partition (BNSP) models provide a variety of strategies for partitioning a D-dimensional space into a set of blocks, such that\r\nthe data within the same block share certain kinds of homogeneity. BNSP models are applicable to many areas, including regression/classification trees, random feature construction, and relational modelling. This survey provides the first comprehensive review of this subject.\r\nWe explore the current progress of BNSP research through  three perspectives: (1) Partition strategies, where we review the various techniques for generating partitions and discuss their theoretical foundation, `self-consistency'; (2) Applications, where we detail the current mainstream usages of BNSP models and identify some potential future applications; and (3) Challenges, where we discuss current unsolved problems and possible avenues for future research.","doi":"10.24963/ijcai.2021/602","url":"https://ijcai.org/proceedings/2021/602","pdf":"https://ijcai.org/proceedings/2021/0602.pdf","markdown":"https://ijcai.org/proceedings/2021/602.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/602","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Survey Track","track":"Survey Track","number":602,"pages":{"first":4408,"last":4415},"keywords":["Machine learning: General"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}