{"type":"ScholarlyArticle","title":"Pushing Forward Marginal MAP with Best-First Search","authors":[{"name":"Radu Marinescu","slug":"radu-marinescu","given":"Radu","family":"Marinescu","affiliation":""},{"name":"Rina Dechter","slug":"rina-dechter","given":"Rina","family":"Dechter","affiliation":""},{"name":"Alexander Ihler","slug":"alexander-ihler","given":"Alexander","family":"Ihler","affiliation":""}],"abstract":"Marginal MAP is known to be a difficult task for graphical models, particularly because the evaluation of each MAP assignment involves a conditional likelihood computation. In order to minimize the number of likelihood evaluations, we focus in this paper on best-first search strategies for exploring the space of partial MAP assignments. We analyze the potential relative benefits of several best-first search algorithms and demonstrate their effectiveness against recent branch and bound schemes through extensive empirical evaluations. Our results show that best-first search improves significantly over existing depth-first approaches, in many cases by several orders of magnitude, especially when guided by relatively weak heuristics.","doi":null,"url":"https://ijcai.org/proceedings/2015/104","pdf":"https://ijcai.org/proceedings/2015/0104.pdf","markdown":"https://ijcai.org/proceedings/2015/104.md","bibtex":"https://ijcai.org/proceedings/2015/bibtex/104","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":"Graphical Models","track":"Main Track","number":104,"pages":{"first":696,"last":701},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}