{"id":"https://openalex.org/W4403582417","doi":"https://doi.org/10.1145/3627673.3679727","title":"Learning Fair Invariant Representations under Covariate and Correlation Shifts Simultaneously","display_name":"Learning Fair Invariant Representations under Covariate and Correlation Shifts Simultaneously","publication_year":2024,"publication_date":"2024-10-20","ids":{"openalex":"https://openalex.org/W4403582417","doi":"https://doi.org/10.1145/3627673.3679727"},"language":"en","primary_location":{"id":"doi:10.1145/3627673.3679727","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3627673.3679727","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Information and Knowledge Management","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/A5115603891","display_name":"Dong Li","orcid":"https://orcid.org/0000-0003-0081-9318"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Dong Li","raw_affiliation_strings":["Tianjin University, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","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":false,"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/A5107863196","display_name":"M. Shao","orcid":"https://orcid.org/0000-0002-6437-2060"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Minglai Shao","raw_affiliation_strings":["Tianjin University, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5115602411","display_name":"Wenjun Wang","orcid":"https://orcid.org/0000-0001-7192-4882"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenjun Wang","raw_affiliation_strings":["Tianjin University, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5115603891"],"corresponding_institution_ids":["https://openalex.org/I162868743"],"apc_list":null,"apc_paid":null,"fwci":1.0878,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.8183254,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1174","last_page":"1183"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9957000017166138,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9957000017166138,"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":0.995199978351593,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.991100013256073,"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/covariate","display_name":"Covariate","score":0.8139896988868713},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.7289652228355408},{"id":"https://openalex.org/keywords/invariant","display_name":"Invariant (physics)","score":0.5657130479812622},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.488192617893219},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.39705294370651245},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36732104420661926},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3651442527770996},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.06875616312026978}],"concepts":[{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.8139896988868713},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.7289652228355408},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.5657130479812622},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.488192617893219},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.39705294370651245},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36732104420661926},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3651442527770996},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.06875616312026978},{"id":"https://openalex.org/C37914503","wikidata":"https://www.wikidata.org/wiki/Q156495","display_name":"Mathematical physics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3627673.3679727","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3627673.3679727","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W200600101","https://openalex.org/W2014352947","https://openalex.org/W2034368206","https://openalex.org/W2100960835","https://openalex.org/W2112796928","https://openalex.org/W2117853077","https://openalex.org/W2155982052","https://openalex.org/W2271514814","https://openalex.org/W2805618604","https://openalex.org/W2894728917","https://openalex.org/W2963043696","https://openalex.org/W2964288524","https://openalex.org/W3120485916","https://openalex.org/W3133542152","https://openalex.org/W3165956705","https://openalex.org/W3208695477","https://openalex.org/W4281492493","https://openalex.org/W4283206275","https://openalex.org/W4287325030","https://openalex.org/W4297094921","https://openalex.org/W4312774735","https://openalex.org/W4385568133","https://openalex.org/W4387846328","https://openalex.org/W6791503542"],"related_works":["https://openalex.org/W2985746494","https://openalex.org/W4206042385","https://openalex.org/W2511384863","https://openalex.org/W2080773131","https://openalex.org/W2096089271","https://openalex.org/W2923628599","https://openalex.org/W2014100433","https://openalex.org/W2051519658","https://openalex.org/W2994787386","https://openalex.org/W2109980432"],"abstract_inverted_index":{"Achieving":[0],"the":[1,31,50],"generalization":[2,81],"of":[3,33],"an":[4],"invariant":[5],"classifier":[6],"from":[7],"training":[8],"domains":[9,13,85],"to":[10,82,139],"shifted":[11],"test":[12,84],"while":[14],"simultaneously":[15],"considering":[16],"model":[17,140],"fairness":[18],"is":[19],"a":[20,58,65,70],"substantial":[21],"and":[22,75,98,116,147],"complex":[23],"challenge":[24],"in":[25,101],"machine":[26],"learning.":[27],"Existing":[28],"methods":[29,136],"address":[30],"problem":[32],"fairness-aware":[34,66,105],"domain":[35],"generalization,":[36],"focusing":[37],"on":[38,63,127],"either":[39],"covariate":[40,74],"shift":[41],"or":[42],"correlation":[43,76],"shift,":[44],"but":[45],"rarely":[46],"consider":[47],"both":[48,73,145],"at":[49],"same":[51],"time.":[52],"In":[53,89],"this":[54],"paper,":[55],"we":[56],"introduce":[57],"novel":[59],"approach":[60,133],"that":[61,131],"focuses":[62],"learning":[64],"domain-invariant":[67,106],"predictor":[68],"within":[69],"framework":[71],"addressing":[72],"shifts":[77],"simultaneously,":[78],"ensuring":[79],"its":[80],"unknown":[83],"inaccessible":[86],"during":[87],"training.":[88],"our":[90,132],"approach,":[91],"data":[92],"are":[93],"first":[94],"disentangled":[95],"into":[96],"content":[97,107],"style":[99],"factors":[100],"latent":[102],"spaces.":[103],"Furthermore,":[104],"representations":[108],"can":[109],"be":[110],"learned":[111],"by":[112],"mitigating":[113],"sensitive":[114],"information":[115,121],"retaining":[117],"as":[118,122,142,144],"much":[119],"other":[120],"possible.":[123],"Extensive":[124],"empirical":[125],"studies":[126],"benchmark":[128],"datasets":[129],"demonstrate":[130],"surpasses":[134],"state-of-the-art":[135],"with":[137],"respect":[138],"accuracy":[141],"well":[143],"group":[146],"individual":[148],"fairness.":[149]},"counts_by_year":[{"year":2025,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}