{"id":"https://openalex.org/W4385568133","doi":"https://doi.org/10.1145/3580305.3599523","title":"Towards Fair Disentangled Online Learning for Changing Environments","display_name":"Towards Fair Disentangled Online Learning for Changing Environments","publication_year":2023,"publication_date":"2023-08-04","ids":{"openalex":"https://openalex.org/W4385568133","doi":"https://doi.org/10.1145/3580305.3599523"},"language":"en","primary_location":{"id":"doi:10.1145/3580305.3599523","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3580305.3599523","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100767050","display_name":"Chen Zhao","orcid":"https://orcid.org/0000-0002-6400-0048"},"institutions":[{"id":"https://openalex.org/I157394403","display_name":"Baylor University","ror":"https://ror.org/005781934","country_code":"US","type":"education","lineage":["https://openalex.org/I157394403"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Chen Zhao","raw_affiliation_strings":["Baylor University, Waco, TX, USA"],"affiliations":[{"raw_affiliation_string":"Baylor University, Waco, TX, USA","institution_ids":["https://openalex.org/I157394403"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114626798","display_name":"Feng Mi","orcid":"https://orcid.org/0000-0002-9648-2301"},"institutions":[{"id":"https://openalex.org/I162577319","display_name":"The University of Texas at Dallas","ror":"https://ror.org/049emcs32","country_code":"US","type":"education","lineage":["https://openalex.org/I162577319"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Feng Mi","raw_affiliation_strings":["University of Texas at Dallas, Richardson, TX, USA"],"affiliations":[{"raw_affiliation_string":"University of Texas at Dallas, Richardson, TX, USA","institution_ids":["https://openalex.org/I162577319"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008463509","display_name":"Xintao Wu","orcid":"https://orcid.org/0000-0002-2823-3063"},"institutions":[{"id":"https://openalex.org/I78715868","display_name":"University of Arkansas at Fayetteville","ror":"https://ror.org/05jbt9m15","country_code":"US","type":"education","lineage":["https://openalex.org/I78715868"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xintao Wu","raw_affiliation_strings":["University of Arkansas, Fayetteville, AR, USA"],"affiliations":[{"raw_affiliation_string":"University of Arkansas, Fayetteville, AR, USA","institution_ids":["https://openalex.org/I78715868"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066407787","display_name":"Kai Jiang","orcid":"https://orcid.org/0009-0008-8053-3443"},"institutions":[{"id":"https://openalex.org/I162577319","display_name":"The University of Texas at Dallas","ror":"https://ror.org/049emcs32","country_code":"US","type":"education","lineage":["https://openalex.org/I162577319"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kai Jiang","raw_affiliation_strings":["The University of Texas at Dallas, Richardson, TX, USA"],"affiliations":[{"raw_affiliation_string":"The University of Texas at Dallas, Richardson, TX, USA","institution_ids":["https://openalex.org/I162577319"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005002693","display_name":"Latifur Khan","orcid":"https://orcid.org/0000-0002-9300-1576"},"institutions":[{"id":"https://openalex.org/I162577319","display_name":"The University of Texas at Dallas","ror":"https://ror.org/049emcs32","country_code":"US","type":"education","lineage":["https://openalex.org/I162577319"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Latifur Khan","raw_affiliation_strings":["The University of Texas at Dallas, Richardson, TX, USA"],"affiliations":[{"raw_affiliation_string":"The University of Texas at Dallas, Richardson, TX, USA","institution_ids":["https://openalex.org/I162577319"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070860161","display_name":"Christan Grant","orcid":"https://orcid.org/0000-0002-6684-3620"},"institutions":[{"id":"https://openalex.org/I33213144","display_name":"University of Florida","ror":"https://ror.org/02y3ad647","country_code":"US","type":"education","lineage":["https://openalex.org/I33213144"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Christan Grant","raw_affiliation_strings":["University of Florida, Gainesville, FL, USA"],"affiliations":[{"raw_affiliation_string":"University of Florida, Gainesville, FL, USA","institution_ids":["https://openalex.org/I33213144"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100352703","display_name":"Feng Chen","orcid":"https://orcid.org/0000-0002-4508-5963"},"institutions":[{"id":"https://openalex.org/I162577319","display_name":"The University of Texas at Dallas","ror":"https://ror.org/049emcs32","country_code":"US","type":"education","lineage":["https://openalex.org/I162577319"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Feng Chen","raw_affiliation_strings":["The University of Texas at Dallas, Richardson, TX, USA"],"affiliations":[{"raw_affiliation_string":"The University of Texas at Dallas, Richardson, TX, USA","institution_ids":["https://openalex.org/I162577319"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5100767050"],"corresponding_institution_ids":["https://openalex.org/I157394403"],"apc_list":null,"apc_paid":null,"fwci":2.5472,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.89752288,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"3480","last_page":"3491"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9955999851226807,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9955999851226807,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10410","display_name":"COVID-19 epidemiological studies","score":0.98089998960495,"subfield":{"id":"https://openalex.org/subfields/2611","display_name":"Modeling and Simulation"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/regret","display_name":"Regret","score":0.9201633930206299},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7611605525016785},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4801746904850006},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47262999415397644},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.46699586510658264},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.4339660108089447},{"id":"https://openalex.org/keywords/invariant","display_name":"Invariant (physics)","score":0.420652836561203},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11837372183799744}],"concepts":[{"id":"https://openalex.org/C50817715","wikidata":"https://www.wikidata.org/wiki/Q79895177","display_name":"Regret","level":2,"score":0.9201633930206299},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7611605525016785},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4801746904850006},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47262999415397644},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.46699586510658264},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.4339660108089447},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.420652836561203},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11837372183799744},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C37914503","wikidata":"https://www.wikidata.org/wiki/Q156495","display_name":"Mathematical physics","level":1,"score":0.0},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3580305.3599523","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3580305.3599523","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W1920962657","https://openalex.org/W2568832377","https://openalex.org/W3135588948","https://openalex.org/W4281492493","https://openalex.org/W4283206275","https://openalex.org/W4287325030","https://openalex.org/W4312774735","https://openalex.org/W6791503542"],"related_works":["https://openalex.org/W4376155396","https://openalex.org/W2971351794","https://openalex.org/W1947085858","https://openalex.org/W2101991911","https://openalex.org/W2174986909","https://openalex.org/W2527791220","https://openalex.org/W2155070487","https://openalex.org/W4311589891","https://openalex.org/W3123835761","https://openalex.org/W118270247"],"abstract_inverted_index":{"In":[0],"the":[1,29,58,97,126,145,184,191],"problem":[2],"of":[3,31,48,186,205,228],"online":[4,79,109],"learning":[5,33,52,110],"for":[6,174,223],"changing":[7,106,131],"environments,":[8],"data":[9,148],"are":[10,121],"sequentially":[11,241],"received":[12],"one":[13],"after":[14],"another":[15],"over":[16],"time,":[17],"and":[18,66,93,125,163,207,226,248],"their":[19,32],"distribution":[20],"assumptions":[21],"may":[22],"vary":[23],"frequently.":[24],"Although":[25],"existing":[26],"methods":[27,244],"demonstrate":[28,237],"effectiveness":[30],"algorithms":[34],"by":[35,96,190,212],"providing":[36],"a":[37,75,87,141,178,193,202,213],"tight":[38],"bound":[39],"on":[40,234],"either":[41],"dynamic":[42,206],"regret":[43,195,209,225],"or":[44],"adaptive":[45],"regret,":[46],"most":[47],"them":[49],"completely":[50],"ignore":[51],"with":[53,86,156],"model":[54,84,187,246],"fairness,":[55],"defined":[56],"as":[57],"statistical":[59],"parity":[60],"across":[61],"different":[62],"sub-population":[63],"(e.g.,":[64],"race":[65],"gender).":[67],"Another":[68],"drawback":[69],"is":[70,91,171,196],"that":[71,105,120,147],"when":[72],"adapting":[73],"to":[74,82,114,123,130],"new":[76],"environment,":[77],"an":[78,159,164],"learner":[80],"needs":[81],"update":[83],"parameters":[85,119,188],"global":[88],"change,":[89],"which":[90,199],"costly":[92],"inefficient.":[94],"Inspired":[95],"sparse":[98],"mechanism":[99],"shift":[100],"hypothesis":[101],"[22],":[102],"we":[103,139],"claim":[104],"environments":[107,124],"in":[108,117,136,198,245],"can":[111,153],"be":[112,154],"attributed":[113],"partial":[115],"changes":[116],"learned":[118],"specific":[122],"rest":[127],"remain":[128],"invariant":[129],"environments.":[132],"To":[133,182],"this":[134,137],"end,":[135],"paper,":[138],"propose":[140],"novel":[142,194],"algorithm":[143],"under":[144,177],"assumption":[146],"collected":[149],"at":[150],"each":[151],"time":[152],"disentangled":[155],"two":[157],"representations,":[158],"environment-invariant":[160],"semantic":[161,169],"factor":[162,170],"environment-specific":[165],"variation":[166],"factor.":[167],"The":[168,217],"further":[172],"used":[173],"fair":[175],"prediction":[176],"group":[179],"fairness":[180,230],"constraint.":[181,216],"evaluate":[183],"sequence":[185],"generated":[189],"learner,":[192],"proposed":[197,239],"it":[200],"takes":[201],"mixed":[203],"form":[204],"static":[208],"metrics":[210],"followed":[211],"fairness-aware":[214],"long-term":[215],"detailed":[218],"analysis":[219],"provides":[220],"theoretical":[221],"guarantees":[222],"loss":[224],"violation":[227],"cumulative":[229],"constraints.":[231],"Empirical":[232],"evaluations":[233],"real-world":[235],"datasets":[236],"our":[238],"method":[240],"outperforms":[242],"baseline":[243],"accuracy":[247],"fairness.":[249]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":6}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
