{"id":"https://openalex.org/W2964883359","doi":"https://doi.org/10.24963/ijcai.2019/838","title":"Learning Interpretable Relational Structures of Hinge-loss Markov Random Fields","display_name":"Learning Interpretable Relational Structures of Hinge-loss Markov Random Fields","publication_year":2019,"publication_date":"2019-07-28","ids":{"openalex":"https://openalex.org/W2964883359","doi":"https://doi.org/10.24963/ijcai.2019/838","mag":"2964883359"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2019/838","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2019/838","pdf_url":"https://www.ijcai.org/proceedings/2019/0838.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.ijcai.org/proceedings/2019/0838.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100333738","display_name":"Yue Zhang","orcid":"https://orcid.org/0000-0002-6327-5023"},"institutions":[{"id":"https://openalex.org/I123946342","display_name":"Binghamton University","ror":"https://ror.org/008rmbt77","country_code":"US","type":"education","lineage":["https://openalex.org/I123946342"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yue Zhang","raw_affiliation_strings":["SUNY Binghamton"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"SUNY Binghamton","institution_ids":["https://openalex.org/I123946342"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101696376","display_name":"Arti Ramesh","orcid":"https://orcid.org/0000-0001-8840-8163"},"institutions":[{"id":"https://openalex.org/I123946342","display_name":"Binghamton University","ror":"https://ror.org/008rmbt77","country_code":"US","type":"education","lineage":["https://openalex.org/I123946342"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Arti Ramesh","raw_affiliation_strings":["SUNY Binghamton"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"SUNY Binghamton","institution_ids":["https://openalex.org/I123946342"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5101696376"],"corresponding_institution_ids":["https://openalex.org/I123946342"],"apc_list":null,"apc_paid":null,"fwci":0.2187,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.51190876,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"6050","last_page":"6056"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9929999709129333,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9811999797821045,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.7325037717819214},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7282320261001587},{"id":"https://openalex.org/keywords/hinge-loss","display_name":"Hinge loss","score":0.7222896814346313},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5883718729019165},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5162898898124695},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.48149573802948},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.4666459560394287},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4638286828994751},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.42524275183677673},{"id":"https://openalex.org/keywords/statistical-relational-learning","display_name":"Statistical relational learning","score":0.41477128863334656},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.2170301079750061},{"id":"https://openalex.org/keywords/relational-database","display_name":"Relational database","score":0.16873306035995483}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.7325037717819214},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7282320261001587},{"id":"https://openalex.org/C39891107","wikidata":"https://www.wikidata.org/wiki/Q5767098","display_name":"Hinge loss","level":3,"score":0.7222896814346313},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5883718729019165},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5162898898124695},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.48149573802948},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.4666459560394287},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4638286828994751},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.42524275183677673},{"id":"https://openalex.org/C177877439","wikidata":"https://www.wikidata.org/wiki/Q7604413","display_name":"Statistical relational learning","level":3,"score":0.41477128863334656},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2170301079750061},{"id":"https://openalex.org/C5655090","wikidata":"https://www.wikidata.org/wiki/Q192588","display_name":"Relational database","level":2,"score":0.16873306035995483},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2019/838","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2019/838","pdf_url":"https://www.ijcai.org/proceedings/2019/0838.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2019/838","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2019/838","pdf_url":"https://www.ijcai.org/proceedings/2019/0838.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.800000011920929,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2964883359.pdf","grobid_xml":"https://content.openalex.org/works/W2964883359.grobid-xml"},"referenced_works_count":37,"referenced_works":["https://openalex.org/W1504069565","https://openalex.org/W1576159843","https://openalex.org/W1700397478","https://openalex.org/W1835243625","https://openalex.org/W1977970897","https://openalex.org/W2021602734","https://openalex.org/W2052148895","https://openalex.org/W2118022153","https://openalex.org/W2121075864","https://openalex.org/W2135958967","https://openalex.org/W2139193890","https://openalex.org/W2144429462","https://openalex.org/W2144461918","https://openalex.org/W2150678881","https://openalex.org/W2169992051","https://openalex.org/W2260756217","https://openalex.org/W2267631676","https://openalex.org/W2572487124","https://openalex.org/W2594103415","https://openalex.org/W2595017202","https://openalex.org/W2606882704","https://openalex.org/W2735089625","https://openalex.org/W2762409054","https://openalex.org/W2787800669","https://openalex.org/W2788485989","https://openalex.org/W2811164316","https://openalex.org/W2898373236","https://openalex.org/W2913787440","https://openalex.org/W2949561945","https://openalex.org/W2963572185","https://openalex.org/W2963798744","https://openalex.org/W2964043796","https://openalex.org/W6677805205","https://openalex.org/W6681092714","https://openalex.org/W6681906880","https://openalex.org/W6791858558","https://openalex.org/W6864014924"],"related_works":["https://openalex.org/W2905433371","https://openalex.org/W2888392564","https://openalex.org/W4310278675","https://openalex.org/W4388422664","https://openalex.org/W4390569940","https://openalex.org/W4361193272","https://openalex.org/W2963326959","https://openalex.org/W4388685194","https://openalex.org/W4312407344","https://openalex.org/W98006832"],"abstract_inverted_index":{"Statistical":[0],"relational":[1],"models":[2,33,45,140],"such":[3],"as":[4],"Markov":[5,11],"logic":[6,21],"networks":[7],"(MLNs)":[8],"and":[9,38,89,111,162,179],"hinge-loss":[10],"random":[12],"fields":[13],"(HL-MRFs)":[14],"are":[15],"specified":[16],"using":[17],"templated":[18],"weighted":[19],"first-order":[20],"clauses,":[22],"leading":[23],"to":[24,31,68,79,86,95,125,141],"the":[25,41,49,54,77,106,123,136,139],"creation":[26],"of":[27,43,52,109,118,138],"complex,":[28],"yet":[29],"easy":[30],"encode":[32],"that":[34,84,103,146],"effectively":[35],"combine":[36],"uncertainty":[37],"logic.":[39],"Learning":[40],"structure":[42],"these":[44],"from":[46,98],"data":[47],"reduces":[48],"human":[50,87],"effort":[51],"identifying":[53],"right":[55],"structures.":[56,73],"In":[57],"this":[58],"work,":[59],"we":[60],"present":[61],"an":[62],"asynchronous":[63,116],"deep":[64],"reinforcement":[65],"learning":[66,101],"algorithm":[67,75,120],"automatically":[69],"learn":[70,80,96,126,142],"HL-MRF":[71],"clause":[72],"Our":[74],"possesses":[76],"ability":[78,124,137],"semantically":[81,143],"meaningful":[82,144],"structures":[83,97,102,128,145],"appeal":[85],"intuition":[88],"understanding,":[90],"while":[91,131],"simultaneously":[92],"being":[93],"able":[94],"data,":[99],"thus":[100],"have":[104],"both":[105],"desirable":[107],"qualities":[108],"interpretability":[110],"good":[112],"prediction":[113,150],"performance.":[114],"The":[115],"nature":[117],"our":[119],"further":[121],"provides":[122],"diverse":[127],"via":[129],"exploration,":[130],"remaining":[132],"scalable.":[133],"We":[134],"demonstrate":[135],"also":[147],"achieve":[148],"better":[149],"performance":[151],"when":[152],"compared":[153],"with":[154],"a":[155,159],"greedy":[156],"search":[157],"algorithm,":[158,161],"path-based":[160],"manually":[163],"defined":[164],"clauses":[165],"on":[166],"two":[167],"computational":[168],"social":[169],"science":[170],"applications:":[171],"i)":[172],"modeling":[173],"recovery":[174],"in":[175],"alcohol":[176],"use":[177],"disorder,":[178],"ii)":[180],"detecting":[181],"bullying.":[182]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
