{"type":"ScholarlyArticle","title":"Multi-Task Multi-Dimensional Hawkes Processes for Modeling Event Sequences","authors":[{"name":"Dixin Luo","slug":"dixin-luo","given":"Dixin","family":"Luo","affiliation":""},{"name":"Hongteng Xu","slug":"hongteng-xu","given":"Hongteng","family":"Xu","affiliation":""},{"name":"Yi Zhen","slug":"yi-zhen","given":"Yi","family":"Zhen","affiliation":""},{"name":"Xia Ning","slug":"xia-ning","given":"Xia","family":"Ning","affiliation":""},{"name":"Hongyuan Zha","slug":"hongyuan-zha","given":"Hongyuan","family":"Zha","affiliation":""},{"name":"Xiaokang Yang","slug":"xiaokang-yang","given":"Xiaokang","family":"Yang","affiliation":""},{"name":"Wenjun Zhang","slug":"wenjun-zhang","given":"Wenjun","family":"Zhang","affiliation":""}],"abstract":"We propose a Multi-task Multi-dimensional Hawkes Process (MMHP) for modeling event sequences where there exist multiple triggering patterns within sequences and structures across sequences.MMHP is able to model the dynamics of multiple sequences jointly by imposing structural constraints and thus systematically uncover clustering structure among sequences.We propose an effective and robust optimization algorithm to learn MMHP models, which takes advantage of alternating direction method of multipliers (ADMM), majorization minimization and Euler-Lagrange equations.Our experimental results demonstrate that MMHP performs well on both synthetic and real data.","doi":null,"url":"https://ijcai.org/proceedings/2015/518","pdf":"https://ijcai.org/proceedings/2015/0518.pdf","markdown":"https://ijcai.org/proceedings/2015/518.md","bibtex":"https://ijcai.org/proceedings/2015/bibtex/518","volume":{"slug":"2015","title":"Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2015","url":"https://ijcai.org/proceedings/2015/","year":2015,"location":"Buenos Aires, Argentina","dates":"25–31 July 2015","editors":"Qiang Yang and Michael Wooldridge","isbn":"978-1-57735-738-4","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Special Track on Machine Learning","track":"Special Track on Machine Learning","number":518,"pages":{"first":3685,"last":3690},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}