{"id":"https://openalex.org/W4318147620","doi":"https://doi.org/10.1109/bigdata55660.2022.10020444","title":"Distributional Shift Adaptation using Domain-Specific Features","display_name":"Distributional Shift Adaptation using Domain-Specific Features","publication_year":2022,"publication_date":"2022-12-17","ids":{"openalex":"https://openalex.org/W4318147620","doi":"https://doi.org/10.1109/bigdata55660.2022.10020444"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata55660.2022.10020444","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata55660.2022.10020444","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Big Data (Big Data)","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/A5016332375","display_name":"Anique Tahir","orcid":"https://orcid.org/0000-0002-2838-147X"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Anique Tahir","raw_affiliation_strings":["Arizona State University,Tempe,AZ,USA","Arizona State University, Tempe, AZ, USA"],"affiliations":[{"raw_affiliation_string":"Arizona State University,Tempe,AZ,USA","institution_ids":["https://openalex.org/I55732556"]},{"raw_affiliation_string":"Arizona State University, Tempe, AZ, USA","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022914600","display_name":"Cheng L\u00fc","orcid":"https://orcid.org/0000-0003-1877-7531"},"institutions":[{"id":"https://openalex.org/I39422238","display_name":"University of Illinois Chicago","ror":"https://ror.org/02mpq6x41","country_code":"US","type":"education","lineage":["https://openalex.org/I39422238"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lu Cheng","raw_affiliation_strings":["University of Illinois Chicago,Chicago,IL,USA","University of Illinois Chicago, Chicago, IL, USA"],"affiliations":[{"raw_affiliation_string":"University of Illinois Chicago,Chicago,IL,USA","institution_ids":["https://openalex.org/I39422238"]},{"raw_affiliation_string":"University of Illinois Chicago, Chicago, IL, USA","institution_ids":["https://openalex.org/I39422238"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054719216","display_name":"Ruocheng Guo","orcid":"https://orcid.org/0000-0002-8522-6142"},"institutions":[{"id":"https://openalex.org/I154935518","display_name":"Royal College of Emergency Medicine","ror":"https://ror.org/04zkbxs23","country_code":"GB","type":"education","lineage":["https://openalex.org/I154935518"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Ruocheng Guo","raw_affiliation_strings":["Bytedance AI Lab,London,UK","Bytedance AI Lab, London, UK"],"affiliations":[{"raw_affiliation_string":"Bytedance AI Lab,London,UK","institution_ids":["https://openalex.org/I154935518"]},{"raw_affiliation_string":"Bytedance AI Lab, London, UK","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100338946","display_name":"Huan Liu","orcid":"https://orcid.org/0000-0002-3264-7904"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Huan Liu","raw_affiliation_strings":["Arizona State University,Tempe,AZ,USA","Arizona State University, Tempe, AZ, USA"],"affiliations":[{"raw_affiliation_string":"Arizona State University,Tempe,AZ,USA","institution_ids":["https://openalex.org/I55732556"]},{"raw_affiliation_string":"Arizona State University, Tempe, AZ, USA","institution_ids":["https://openalex.org/I55732556"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5016332375"],"corresponding_institution_ids":["https://openalex.org/I55732556"],"apc_list":null,"apc_paid":null,"fwci":0.3141,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.54385965,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"5593","last_page":"5597"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9911999702453613,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9911999702453613,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9868999719619751,"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":0.9782999753952026,"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/computer-science","display_name":"Computer science","score":0.7640962600708008},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.7293730974197388},{"id":"https://openalex.org/keywords/invariant","display_name":"Invariant (physics)","score":0.6913648843765259},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.618187665939331},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5898949503898621},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5630053877830505},{"id":"https://openalex.org/keywords/rendering","display_name":"Rendering (computer graphics)","score":0.5274907350540161},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.44278639554977417},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4239400625228882},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.41753309965133667},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13996639847755432},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.12005984783172607}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7640962600708008},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.7293730974197388},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.6913648843765259},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.618187665939331},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5898949503898621},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5630053877830505},{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.5274907350540161},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.44278639554977417},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4239400625228882},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.41753309965133667},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13996639847755432},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.12005984783172607},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"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/C37914503","wikidata":"https://www.wikidata.org/wiki/Q156495","display_name":"Mathematical physics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata55660.2022.10020444","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata55660.2022.10020444","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","score":0.6399999856948853,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320337345","display_name":"Office of Naval Research","ror":"https://ror.org/00rk2pe57"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W1731081199","https://openalex.org/W1821462560","https://openalex.org/W2068181924","https://openalex.org/W2155195660","https://openalex.org/W2563173212","https://openalex.org/W2753738274","https://openalex.org/W2798658180","https://openalex.org/W2889232360","https://openalex.org/W2968009374","https://openalex.org/W2978017171","https://openalex.org/W2978426779","https://openalex.org/W2991497298","https://openalex.org/W3001197829","https://openalex.org/W3034368386","https://openalex.org/W3035160371","https://openalex.org/W3035682985","https://openalex.org/W3038745855","https://openalex.org/W3138154797","https://openalex.org/W4235198123","https://openalex.org/W4287073474","https://openalex.org/W4287728573","https://openalex.org/W4288287305","https://openalex.org/W4312774735","https://openalex.org/W6617145748","https://openalex.org/W6637618735","https://openalex.org/W6638523607","https://openalex.org/W6682891771","https://openalex.org/W6713955831","https://openalex.org/W6744627333","https://openalex.org/W6748716324","https://openalex.org/W6756663807","https://openalex.org/W6762913911","https://openalex.org/W6765285020","https://openalex.org/W6766132365","https://openalex.org/W6768851824","https://openalex.org/W6771271773","https://openalex.org/W6773005947","https://openalex.org/W6780233385","https://openalex.org/W6780505495","https://openalex.org/W6786798588","https://openalex.org/W6790967031","https://openalex.org/W6799405210","https://openalex.org/W6805139958"],"related_works":["https://openalex.org/W2582295320","https://openalex.org/W2060809589","https://openalex.org/W3109786615","https://openalex.org/W2174759944","https://openalex.org/W2990948995","https://openalex.org/W2081348959","https://openalex.org/W2531741693","https://openalex.org/W3158596343","https://openalex.org/W4285322112","https://openalex.org/W4292794239"],"abstract_inverted_index":{"Machine":[0],"learning":[1],"algorithms":[2,31],"typically":[3],"assume":[4],"that":[5,52,76,95,133,146],"the":[6,13,36,62,73,85,104,113,137,147,152],"training":[7,46],"and":[8,81],"test":[9],"samples":[10,117],"come":[11],"from":[12],"same":[14],"distributions,":[15],"i.e.,":[16],"in-distribution.":[17],"However,":[18],"in":[19,61,72,99],"open-world":[20],"scenarios,":[21],"streaming":[22],"big":[23],"data":[24],"can":[25],"be":[26],"Out-Of-Distribution":[27],"(OOD),":[28],"rendering":[29],"these":[30,53],"ineffective.":[32],"Prior":[33],"solutions":[34],"to":[35,40,84,126,136],"OOD":[37,121],"challenge":[38],"seek":[39],"identify":[41],"invariant":[42,54,79,107],"features":[43,55,75,80,82,105],"across":[44],"different":[45],"domains.":[47],"The":[48],"underlying":[49],"assumption":[50],"is":[51,70,149],"should":[56],"also":[57],"work":[58,69],"reasonably":[59],"well":[60],"unlabeled":[63],"target":[64,86,138],"domain.":[65,87,139],"By":[66],"contrast,":[67],"this":[68],"interested":[71],"domain-specific":[74],"include":[77],"both":[78],"unique":[83],"We":[88],"propose":[89],"a":[90,128],"simple":[91],"yet":[92],"effective":[93],"approach":[94,111],"relies":[96],"on":[97,142],"correlations":[98],"general":[100],"regardless":[101],"of":[102],"whether":[103],"are":[106],"or":[108],"not.":[109],"Our":[110],"uses":[112],"most":[114],"confidently":[115],"predicted":[116],"identified":[118],"by":[119,154],"an":[120],"base":[122],"model":[123,130],"(teacher":[124],"model)":[125,132],"train":[127],"new":[129],"(student":[131],"effectively":[134],"adapts":[135],"Empirical":[140],"evaluations":[141],"benchmark":[143],"datasets":[144],"show":[145],"performance":[148],"improved":[150],"over":[151],"SOTA":[153],"\u223c10-20%.":[155]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":2}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
