{"id":"https://openalex.org/W7161602862","doi":"https://doi.org/10.48550/arxiv.2605.15586","title":"Embracing Biased Transition Matrices for Complementary-Label Learning with Many Classes","display_name":"Embracing Biased Transition Matrices for Complementary-Label Learning with Many Classes","publication_year":2026,"publication_date":"2026-05-15","ids":{"openalex":"https://openalex.org/W7161602862","doi":"https://doi.org/10.48550/arxiv.2605.15586"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.15586","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.15586","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.15586","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136360488","display_name":"Tan-Ha Mai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mai, Tan-Ha","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113972959","display_name":"Chao-Kai Chiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chiang, Chao-Kai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136365532","display_name":"Han-Hwa Shih","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shih, Han-Hwa","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136411784","display_name":"Gang Niu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Niu, Gang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136368810","display_name":"Masashi Sugiyama","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sugiyama, Masashi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136443507","display_name":"Hsuan-Tien Lin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lin, Hsuan-Tien","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.6140999794006348,"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":0.6140999794006348,"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":0.21870000660419464,"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":0.010999999940395355,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5806000232696533},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.4514000117778778},{"id":"https://openalex.org/keywords/transition","display_name":"Transition (genetics)","score":0.4487999975681305},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.375900000333786},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.37220001220703125},{"id":"https://openalex.org/keywords/active-learning","display_name":"Active learning (machine learning)","score":0.329800009727478}],"concepts":[{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5806000232696533},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5633000135421753},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5462999939918518},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.4514000117778778},{"id":"https://openalex.org/C194232998","wikidata":"https://www.wikidata.org/wiki/Q1606712","display_name":"Transition (genetics)","level":3,"score":0.4487999975681305},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40639999508857727},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.375900000333786},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.37220001220703125},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3353999853134155},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.329800009727478},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3273000121116638},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.27720001339912415},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.2741999924182892},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.2651999890804291}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.15586","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.15586","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.15586","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.15586","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Complementary-label":[0],"learning":[1,62,121],"(CLL)":[2],"is":[3],"a":[4,20,82,92,106,138],"weakly":[5],"supervised":[6],"paradigm":[7],"where":[8],"instances":[9],"are":[10],"labeled":[11],"with":[12,32],"classes":[13,147],"they":[14],"do":[15],"not":[16],"belong":[17],"to.":[18],"Despite":[19],"decade":[21],"of":[22,51,94],"research,":[23],"CLL":[24,143],"methods":[25],"remain":[26],"competitive":[27],"mainly":[28],"on":[29,122],"10-class":[30],"classification,":[31],"scaling":[33],"to":[34,39,91,100,112],"large":[35],"label":[36,53],"spaces":[37],"continuing":[38],"be":[40,77],"an":[41],"enduring":[42],"bottleneck.":[43],"This":[44,96],"limitation":[45],"stems":[46],"from":[47],"the":[48,61],"common":[49],"assumption":[50],"uniform":[52],"generation":[54,85],"in":[55,64,148],"traditional":[56,133],"methods,":[57],"which":[58],"fatally":[59],"dilutes":[60],"signal":[63],"many-class":[65],"settings.":[66],"In":[67],"this":[68,73,116],"paper,":[69],"we":[70],"demonstrate":[71],"that":[72,87,114],"long-standing":[74],"barrier":[75],"can":[76],"overcome":[78],"by":[79],"deliberately":[80],"designing":[81],"biased":[83],"(non-uniform)":[84],"process":[86],"restricts":[88],"complementary":[89],"labels":[90],"subset":[93],"classes.":[95],"finding":[97],"motivates":[98],"us":[99],"propose":[101],"Bias-Induced":[102],"Constrained":[103],"Labeling":[104],"(BICL),":[105],"principled":[107],"framework":[108],"spanning":[109],"data":[110],"collection":[111],"training":[113],"leverages":[115],"bias.":[117],"BICL":[118],"enables":[119],"effective":[120],"CIFAR-100":[123],"and":[124],"TinyImageNet-200,":[125],"achieving":[126],"more":[127],"than":[128],"sevenfold":[129],"accuracy":[130],"improvements":[131],"over":[132],"methods.":[134],"Our":[135],"findings":[136],"establish":[137],"new":[139],"trajectory":[140],"for":[141,145],"making":[142],"feasible":[144],"many":[146],"real-world":[149],"applications.":[150]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-19T00:00:00"}
