{"id":"https://openalex.org/W7138886070","doi":"https://doi.org/10.1609/aaai.v40i42.40899","title":"Robust Learning from Noisily Labeled Long-Tailed Data via Fairness Regularizer","display_name":"Robust Learning from Noisily Labeled Long-Tailed Data via Fairness Regularizer","publication_year":2026,"publication_date":"2026-03-14","ids":{"openalex":"https://openalex.org/W7138886070","doi":"https://doi.org/10.1609/aaai.v40i42.40899"},"language":null,"primary_location":{"id":"doi:10.1609/aaai.v40i42.40899","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i42.40899","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.1609/aaai.v40i42.40899","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129859775","display_name":"Jiaheng Wei","orcid":null},"institutions":[{"id":"https://openalex.org/I200769079","display_name":"Hong Kong University of Science and Technology","ror":"https://ror.org/00q4vv597","country_code":"HK","type":"education","lineage":["https://openalex.org/I200769079"]}],"countries":["HK"],"is_corresponding":true,"raw_author_name":"Jiaheng Wei","raw_affiliation_strings":["The Hong Kong University of Science and Technology"],"affiliations":[{"raw_affiliation_string":"The Hong Kong University of Science and Technology","institution_ids":["https://openalex.org/I200769079"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129893975","display_name":"Zhaowei Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210124192","display_name":"Delhi Development Authority","ror":"https://ror.org/031eap517","country_code":"IN","type":"government","lineage":["https://openalex.org/I4210124192"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Zhaowei Zhu","raw_affiliation_strings":["D5Data"],"affiliations":[{"raw_affiliation_string":"D5Data","institution_ids":["https://openalex.org/I4210124192"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129932071","display_name":"Gang Niu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210110652","display_name":"RIKEN","ror":"https://ror.org/01sjwvz98","country_code":"JP","type":"facility","lineage":["https://openalex.org/I4210110652"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Gang Niu","raw_affiliation_strings":["RIKEN"],"affiliations":[{"raw_affiliation_string":"RIKEN","institution_ids":["https://openalex.org/I4210110652"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065250332","display_name":"Tongliang Liu","orcid":"https://orcid.org/0000-0002-9640-6472"},"institutions":[{"id":"https://openalex.org/I129604602","display_name":"The University of Sydney","ror":"https://ror.org/0384j8v12","country_code":"AU","type":"education","lineage":["https://openalex.org/I129604602"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Tongliang Liu","raw_affiliation_strings":["The University of Sydney"],"affiliations":[{"raw_affiliation_string":"The University of Sydney","institution_ids":["https://openalex.org/I129604602"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100321839","display_name":"Sijia Liu","orcid":"https://orcid.org/0000-0002-9214-6861"},"institutions":[{"id":"https://openalex.org/I87216513","display_name":"Michigan State University","ror":"https://ror.org/05hs6h993","country_code":"US","type":"education","lineage":["https://openalex.org/I87216513"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sijia Liu","raw_affiliation_strings":["Michigan State University"],"affiliations":[{"raw_affiliation_string":"Michigan State University","institution_ids":["https://openalex.org/I87216513"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130045141","display_name":"Masashi Sugiyama","orcid":null},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Masashi Sugiyama","raw_affiliation_strings":["RIKEN\nThe University of Tokyo"],"affiliations":[{"raw_affiliation_string":"RIKEN\nThe University of Tokyo","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5129917251","display_name":"Yang Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I185103710","display_name":"University of California, Santa Cruz","ror":"https://ror.org/03s65by71","country_code":"US","type":"education","lineage":["https://openalex.org/I185103710"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yang Liu","raw_affiliation_strings":["University of California, Santa Cruz"],"affiliations":[{"raw_affiliation_string":"University of California, Santa Cruz","institution_ids":["https://openalex.org/I185103710"]}]}],"institutions":[],"countries_distinct_count":5,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5129859775"],"corresponding_institution_ids":["https://openalex.org/I200769079"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.78426124,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"40","issue":"42","first_page":"35847","last_page":"35856"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9760000109672546,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9760000109672546,"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/T10057","display_name":"Face and Expression Recognition","score":0.004699999932199717,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11550","display_name":"Text and Document Classification Technologies","score":0.0015999999595806003,"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/coupling","display_name":"Coupling (piping)","score":0.49390000104904175},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.3278000056743622},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.30160000920295715},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.29179999232292175},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.2849000096321106}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6694999933242798},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.557200014591217},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5289000272750854},{"id":"https://openalex.org/C131584629","wikidata":"https://www.wikidata.org/wiki/Q4308705","display_name":"Coupling (piping)","level":2,"score":0.49390000104904175},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.3278000056743622},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3116999864578247},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.30160000920295715},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.29179999232292175},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2849000096321106},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.28040000796318054},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2651999890804291},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.25519999861717224},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2540999948978424}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1609/aaai.v40i42.40899","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i42.40899","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1609/aaai.v40i42.40899","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i42.40899","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Both":[0],"long-tailed":[1,55],"and":[2,11,27,128],"noisily":[3],"labeled":[4],"data":[5],"frequently":[6],"appear":[7],"in":[8,23,76],"real-world":[9],"applications":[10],"impose":[12],"significant":[13],"challenges":[14],"for":[15],"learning.":[16],"Most":[17],"prior":[18],"works":[19],"treat":[20],"either":[21],"problem":[22],"an":[24],"isolated":[25],"way":[26],"do":[28,68],"not":[29,69],"explicitly":[30],"consider":[31],"the":[32,36,49,52,61,82,88,99,106,116,121,126,129,136,139],"coupling":[33],"effects":[34],"of":[35,63,84,90,123,138],"two.":[37],"Our":[38],"empirical":[39],"observation":[40],"reveals":[41],"that":[42,115],"such":[43],"solutions":[44],"fail":[45],"to":[46],"consistently":[47],"improve":[48],"learning":[50,131],"when":[51,142],"dataset":[53],"is":[54],"with":[56,60,144],"label":[57,64],"noise.":[58],"Moreover,":[59],"presence":[62],"noise,":[65],"existing":[66,146],"methods":[67],"observe":[70],"universal":[71],"improvements":[72],"across":[73],"different":[74],"sub-populations;":[75],"other":[77],"words,":[78],"some":[79],"sub-populations":[80,124],"enjoyed":[81],"benefits":[83],"improved":[85],"accuracy":[86],"at":[87],"cost":[89],"hurting":[91],"others.":[92],"Based":[93],"on":[94,125],"these":[95],"observations,":[96],"we":[97],"introduce":[98],"Fairness":[100],"Regularizer":[101],"(FR),":[102],"inspired":[103],"by":[104],"regularizing":[105],"performance":[107],"gap":[108],"between":[109],"any":[110],"two":[111],"sub-populations.":[112],"We":[113],"show":[114],"introduced":[117],"fairness":[118],"regularizer":[119],"improves":[120],"performances":[122],"tail":[127],"overall":[130],"performance.":[132],"Extensive":[133],"experiments":[134],"demonstrate":[135],"effectiveness":[137],"proposed":[140],"solution":[141],"complemented":[143],"certain":[145],"popular":[147],"robust":[148],"or":[149],"class-balanced":[150],"methods.":[151]},"counts_by_year":[],"updated_date":"2026-03-20T20:54:20.808490","created_date":"2026-03-20T00:00:00"}