{"type":"ScholarlyArticle","title":"Distributed Pareto Optimization for Subset Selection","authors":[{"name":"Chao Qian","slug":"chao-qian","given":"Chao","family":"Qian","affiliation":"Anhui Province Key Lab of Big Data Analysis and Application , University of Science and Technology of China , Hefei 230027 , China"},{"name":"Guiying Li","slug":"guiying-li","given":"Guiying","family":"Li","affiliation":"Anhui Province Key Lab of Big Data Analysis and Application , University of Science and Technology of China , Hefei 230027 , China"},{"name":"Chao Feng","slug":"chao-feng","given":"Chao","family":"Feng","affiliation":"Anhui Province Key Lab of Big Data Analysis and Application , University of Science and Technology of China , Hefei 230027 , China"},{"name":"Ke Tang","slug":"ke-tang","given":"Ke","family":"Tang","affiliation":"Shenzhen Key Lab of Computational Intelligence , Department of Computer Science and Engineering , Southern University of Science and Technology , Shenzhen 518055 , China"}],"abstract":"The subset selection problem that selects a few items from a ground set arises in many applications such as maximum coverage, influence maximization, sparse regression, etc. The recently proposed POSS algorithm is a powerful approximation solver for this problem. However, POSS requires centralized access to the full ground set, and thus is impractical for large-scale real-world applications, where the ground set is too large to be stored on one single machine. In this paper, we propose a distributed version of POSS (DPOSS) with a bounded approximation guarantee. DPOSS can be easily implemented in the MapReduce framework. Our extensive experiments using Spark, on various real-world data sets with size ranging from thousands to millions, show that DPOSS can achieve competitive performance compared with the centralized POSS, and is almost always better than the state-of-the-art distributed greedy algorithm RandGreeDi.","doi":"10.24963/ijcai.2018/207","url":"https://ijcai.org/proceedings/2018/207","pdf":"https://ijcai.org/proceedings/2018/0207.pdf","markdown":"https://ijcai.org/proceedings/2018/207.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/207","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Heuristic Search and Game Playing","track":"Main track","number":207,"pages":{"first":1492,"last":1498},"keywords":["Heuristic Search and Game Playing: Heuristic Search and Machine Learning","Heuristic Search and Game Playing: Combinatorial Search and Optimisation","Heuristic Search and Game Playing: Heuristic Search","Heuristic Search and Game Playing: Distributed Search"],"videos":[],"references":[{"position":1,"raw":"[Barbosa et al., 2015] R. 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