{"id":"https://openalex.org/W4401863561","doi":"https://doi.org/10.1145/3637528.3671756","title":"Asymmetric Beta Loss for Evidence-Based Safe Semi-Supervised Multi-Label Learning","display_name":"Asymmetric Beta Loss for Evidence-Based Safe Semi-Supervised Multi-Label Learning","publication_year":2024,"publication_date":"2024-08-24","ids":{"openalex":"https://openalex.org/W4401863561","doi":"https://doi.org/10.1145/3637528.3671756"},"language":"en","primary_location":{"id":"doi:10.1145/3637528.3671756","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3637528.3671756","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3637528.3671756","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3637528.3671756","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Hao-Zhe Liu","orcid":"https://orcid.org/0009-0000-8362-3032"},"institutions":[{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hao-Zhe Liu","raw_affiliation_strings":["Nanjing University of Aeronautics and Astronautics, Nanjing, China"],"raw_orcid":"https://orcid.org/0009-0000-8362-3032","affiliations":[{"raw_affiliation_string":"Nanjing University of Aeronautics and Astronautics, Nanjing, China","institution_ids":["https://openalex.org/I9842412"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085720255","display_name":"Ming-Kun Xie","orcid":"https://orcid.org/0000-0002-1053-1409"},"institutions":[{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ming-Kun Xie","raw_affiliation_strings":["Nanjing University of Aeronautics and Astronautics, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-1053-1409","affiliations":[{"raw_affiliation_string":"Nanjing University of Aeronautics and Astronautics, Nanjing, China","institution_ids":["https://openalex.org/I9842412"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089158573","display_name":"Chen-Chen Zong","orcid":"https://orcid.org/0000-0003-3588-1461"},"institutions":[{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chen-Chen Zong","raw_affiliation_strings":["Nanjing University of Aeronautics and Astronautics, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0003-3588-1461","affiliations":[{"raw_affiliation_string":"Nanjing University of Aeronautics and Astronautics, Nanjing, China","institution_ids":["https://openalex.org/I9842412"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103204774","display_name":"Sheng-Jun Huang","orcid":"https://orcid.org/0000-0002-7673-5367"},"institutions":[{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sheng-Jun Huang","raw_affiliation_strings":["Nanjing University of Aeronautics and Astronautics, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-7673-5367","affiliations":[{"raw_affiliation_string":"Nanjing University of Aeronautics and Astronautics, Nanjing, China","institution_ids":["https://openalex.org/I9842412"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I9842412"],"apc_list":null,"apc_paid":null,"fwci":0.3543,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.5326849,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1909","last_page":"1920"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9988999962806702,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9988999962806702,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9944999814033508,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9825000166893005,"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/robustness","display_name":"Robustness (evolution)","score":0.7722736597061157},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.766209602355957},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6552343368530273},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6455974578857422},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.6440830230712891},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.550981342792511},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-supervised learning","score":0.5220180153846741},{"id":"https://openalex.org/keywords/dual","display_name":"Dual (grammatical number)","score":0.5163708925247192},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.49834752082824707},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.48771604895591736},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3730740547180176},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.14246323704719543}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7722736597061157},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.766209602355957},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6552343368530273},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6455974578857422},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.6440830230712891},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.550981342792511},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.5220180153846741},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.5163708925247192},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.49834752082824707},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.48771604895591736},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3730740547180176},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.14246323704719543},{"id":"https://openalex.org/C124952713","wikidata":"https://www.wikidata.org/wiki/Q8242","display_name":"Literature","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3637528.3671756","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3637528.3671756","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3637528.3671756","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3637528.3671756","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3637528.3671756","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3637528.3671756","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3295706626","display_name":null,"funder_award_id":"62222605","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4085819169","display_name":null,"funder_award_id":"BK20222012","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G426506487","display_name":null,"funder_award_id":"2020AAA0107000","funder_id":"https://openalex.org/F4320329860","funder_display_name":"National Science and Technology Major Project"},{"id":"https://openalex.org/G8593002909","display_name":null,"funder_award_id":"BK20211517","funder_id":"https://openalex.org/F4320322769","funder_display_name":"Natural Science Foundation of Jiangsu Province"},{"id":"https://openalex.org/G8767179593","display_name":null,"funder_award_id":"BK20222012","funder_id":"https://openalex.org/F4320322769","funder_display_name":"Natural Science Foundation of Jiangsu Province"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321605","display_name":"Government of Jiangsu Province","ror":"https://ror.org/004svx814"},{"id":"https://openalex.org/F4320322769","display_name":"Natural Science Foundation of Jiangsu Province","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320329860","display_name":"National Science and Technology Major Project","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4401863561.pdf","grobid_xml":"https://content.openalex.org/works/W4401863561.grobid-xml"},"referenced_works_count":17,"referenced_works":["https://openalex.org/W2007972815","https://openalex.org/W2037227137","https://openalex.org/W2108598243","https://openalex.org/W2559655401","https://openalex.org/W2788462285","https://openalex.org/W2997131443","https://openalex.org/W2998214075","https://openalex.org/W3133128010","https://openalex.org/W4214671648","https://openalex.org/W4214673031","https://openalex.org/W4221162144","https://openalex.org/W4234587807","https://openalex.org/W4281720556","https://openalex.org/W4307959803","https://openalex.org/W4313138032","https://openalex.org/W4385482626","https://openalex.org/W4385690813"],"related_works":["https://openalex.org/W4312414840","https://openalex.org/W2794908468","https://openalex.org/W4206276646","https://openalex.org/W2943467239","https://openalex.org/W1571801203","https://openalex.org/W101422005","https://openalex.org/W192740413","https://openalex.org/W3004135598","https://openalex.org/W2952937263","https://openalex.org/W2131153761"],"abstract_inverted_index":{"The":[0,159],"goal":[1,90],"of":[2,16,83,91,115,203],"semi-supervised":[3,75],"multi-label":[4,76],"learning":[5,77,86],"(SSMLL)":[6],"is":[7],"to":[8,26,61,108,123,141,152,163,171,189],"improve":[9],"model":[10,66],"performance":[11],"by":[12,131],"leveraging":[13],"the":[14,23,32,40,81,89,104,113,125,132,138,167,177,201,204],"information":[15],"unlabeled":[17,28,37,47,96,192],"data.":[18],"Recent":[19],"studies":[20],"usually":[21],"adopt":[22,148],"pseudo-labeling":[24],"strategy":[25],"tackle":[27],"data":[29,38,97],"based":[30,79],"on":[31,80,196],"assumption":[33],"that":[34],"labeled":[35],"and":[36,94,156,211],"share":[39],"same":[41],"distribution.":[42],"However,":[43],"in":[44,65,117,135,207],"realistic":[45],"scenarios,":[46],"examples":[48],"are":[49,161,169],"often":[50],"collected":[51],"through":[52],"cost-effective":[53],"methods,":[54],"inevitably":[55],"introducing":[56],"out-of-distribution":[57],"(OOD)":[58],"data,":[59],"leading":[60],"a":[62,73,143,149,184],"significant":[63],"decline":[64],"performance.":[67],"In":[68],"this":[69],"paper,":[70],"we":[71,102,147,182],"propose":[72,103],"safe":[74],"framework":[78],"theory":[82],"evidential":[84],"deep":[85],"(EDL),":[87],"with":[88],"achieving":[92],"robust":[93,144],"effective":[95],"exploitation.":[98],"On":[99,137],"one":[100],"hand,":[101,140],"asymmetric":[105],"beta":[106],"loss":[107,187],"not":[109],"only":[110],"compensate":[111],"for":[112,179],"lack":[114],"robustness":[116],"common":[118],"MLL":[119],"losses,":[120],"but":[121],"also":[122],"solve":[124],"inherent":[126],"positive-negative":[127],"imbalance":[128],"problem":[129],"faced":[130],"EDL":[133],"losses":[134],"MLL.":[136],"other":[139],"construct":[142],"SSMLL":[145,212],"framework,":[146],"dual-head":[150],"structure":[151],"generate":[153,164],"class":[154],"probabilities":[155],"instance":[157],"uncertainties.":[158],"former":[160],"used":[162],"pseudo-labels,":[165],"while":[166],"latter":[168],"utilized":[170],"filter":[172],"OOD":[173,209],"examples.":[174],"To":[175],"avoid":[176],"need":[178],"threshold":[180],"estimation,":[181],"develop":[183],"dual-measurement":[185],"weighted":[186],"function":[188],"safely":[190],"perform":[191],"training.":[193],"Extensive":[194],"experiments":[195],"multiple":[197],"benchmark":[198],"datasets":[199],"verify":[200],"effectiveness":[202],"proposed":[205],"method":[206],"both":[208],"detection":[210],"tasks.":[213]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-31T08:31:51.225901","created_date":"2025-10-10T00:00:00"}
