{"id":"https://openalex.org/W3035658740","doi":null,"title":"Match and Reweight Strategy for Generalized Target Shift","display_name":"Match and Reweight Strategy for Generalized Target Shift","publication_year":2020,"publication_date":"2020-06-12","ids":{"openalex":"https://openalex.org/W3035658740","mag":"3035658740"},"language":"en","primary_location":{"id":"mag:3035658740","doi":null,"is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/2006.08161","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://export.arxiv.org/pdf/2006.08161","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5031137438","display_name":"Alain Rakotomamonjy","orcid":"https://orcid.org/0000-0002-4210-7792","observed_orcids":["https://orcid.org/0000-0002-4210-7792"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alain Rakotomamonjy","raw_affiliation_strings":["Equipe Apprentissage"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Equipe Apprentissage","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051249358","display_name":"R\u00e9mi Flamary","orcid":"https://orcid.org/0000-0002-4212-6627","observed_orcids":["https://orcid.org/0000-0002-4212-6627"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"R\u00e9mi Flamary","raw_affiliation_strings":["Joseph Louis LAGRANGE"],"raw_orcid":"https://orcid.org/0000-0002-4212-6627","affiliations":[{"raw_affiliation_string":"Joseph Louis LAGRANGE","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003774172","display_name":"Gilles Gasso","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gilles Gasso","raw_affiliation_strings":["Equipe Apprentissage"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Equipe Apprentissage","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079768821","display_name":"Mokhtar Z. Alaya","orcid":"https://orcid.org/0000-0002-1103-6944","observed_orcids":["https://orcid.org/0000-0002-1103-6944"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mokhtar Z. Alaya","raw_affiliation_strings":["Equipe Apprentissage"],"raw_orcid":"https://orcid.org/0000-0002-1103-6944","affiliations":[{"raw_affiliation_string":"Equipe Apprentissage","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064763619","display_name":"Maxime B\u00e9rar","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Maxime B\u00e9rar","raw_affiliation_strings":["Equipe Apprentissage"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Equipe Apprentissage","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111895517","display_name":"Nicolas Courty","orcid":"https://orcid.org/0000-0003-1353-0126","observed_orcids":["https://orcid.org/0000-0003-1353-0126"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nicolas Courty","raw_affiliation_strings":["Environment observation with complex imagery"],"raw_orcid":"https://orcid.org/0000-0003-1353-0126","affiliations":[{"raw_affiliation_string":"Environment observation with complex imagery","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":1.0,"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":1.0,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.0,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.0,"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/unsupervised-domain-adaptation","display_name":"unsupervised domain adaptation","score":0.9679999947547913},{"id":"https://openalex.org/keywords/label-shift","display_name":"label shift","score":0.9980000257492065},{"id":"https://openalex.org/keywords/importance-weighting","display_name":"importance weighting","score":0.5149999856948853},{"id":"https://openalex.org/keywords/optimal-transport","display_name":"optimal transport","score":0.8450000286102295},{"id":"https://openalex.org/keywords/wasserstein-distance","display_name":"Wasserstein distance","score":0.9710000157356262},{"id":"https://openalex.org/keywords/domain-generalization","display_name":"domain generalization","score":0.527999997138977}],"concepts":[{"id":"https://openalex.org/C55439883","wikidata":"https://www.wikidata.org/wiki/Q360812","display_name":"Correctness","level":2,"score":0.7744331359863281},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.7513145208358765},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.641334593296051},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.6027011871337891},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5763447284698486},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.5377601981163025},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5320063233375549},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4320067763328552},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.42738038301467896},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4018954932689667},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3919731378555298},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"mag:3035658740","doi":null,"is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/2006.08161","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"pmh:oai:HAL:hal-02866979v1","doi":null,"is_oa":true,"landing_page_url":"https://hal.archives-ouvertes.fr/hal-02866979","pdf_url":null,"source":null,"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"https://hal.archives-ouvertes.fr/hal-02866979","raw_type":"info:eu-repo/semantics/preprint"}],"best_oa_location":{"id":"mag:3035658740","doi":null,"is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/2006.08161","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W126423635","https://openalex.org/W189742998","https://openalex.org/W304676660","https://openalex.org/W305865050","https://openalex.org/W1565327149","https://openalex.org/W1617650991","https://openalex.org/W1731081199","https://openalex.org/W1882958252","https://openalex.org/W2010072035","https://openalex.org/W2101807845","https://openalex.org/W2111272908","https://openalex.org/W2112483442","https://openalex.org/W2140956445","https://openalex.org/W2153929442","https://openalex.org/W2159291411","https://openalex.org/W2463241543","https://openalex.org/W2605135824","https://openalex.org/W2766897166","https://openalex.org/W2770645414","https://openalex.org/W2785542264","https://openalex.org/W2790341021","https://openalex.org/W2793477525","https://openalex.org/W2810676764","https://openalex.org/W2899486018","https://openalex.org/W2910300272","https://openalex.org/W2920306970","https://openalex.org/W2921110161","https://openalex.org/W2921464306","https://openalex.org/W2950362879","https://openalex.org/W2951111165","https://openalex.org/W2959656158","https://openalex.org/W2963181447","https://openalex.org/W2964285681","https://openalex.org/W3003692528","https://openalex.org/W3006477733","https://openalex.org/W3106304233","https://openalex.org/W3199072389","https://openalex.org/W4255839052"],"related_works":["https://openalex.org/W3131754697","https://openalex.org/W4286894655","https://openalex.org/W3012148446","https://openalex.org/W3107385679","https://openalex.org/W3208439591","https://openalex.org/W2153929442","https://openalex.org/W4309088539","https://openalex.org/W2790341021","https://openalex.org/W2910300272","https://openalex.org/W4319451950"],"abstract_inverted_index":{"We":[0,21,95],"address":[1],"the":[2,9,45,76],"problem":[3],"of":[4,11,55,72],"unsupervised":[5],"domain":[6,126],"adaptation":[7,127],"under":[8],"setting":[10],"generalized":[12],"target":[13,39,56],"shift":[14],"(both":[15],"class-conditional":[16,46],"and":[17,38,42,63,88,131],"label":[18,40,57],"shifts":[19],"occur).":[20],"show":[22,113],"that":[23,25,97,114],"in":[24],"setting,":[26],"for":[27],"good":[28],"generalization,":[29],"it":[30],"is":[31],"necessary":[32],"to":[33,43,101],"learn":[34,79],"with":[35,69],"similar":[36],"source":[37],"distributions":[41],"match":[44,102],"probabilities.":[47],"For":[48],"this":[49,98],"purpose,":[50],"we":[51,78],"propose":[52],"an":[53],"estimation":[54,62,67],"proportion":[58],"by":[59,82],"blending":[60],"mixture":[61],"optimal":[64],"transport.":[65],"This":[66],"comes":[68],"theoretical":[70],"guarantees":[71],"correctness.":[73],"Based":[74],"on":[75,107,119],"estimation,":[77],"a":[80,84,89,124],"model":[81],"minimizing":[83],"importance":[85],"weighted":[86,93],"loss":[87],"Wasserstein":[90],"distance":[91],"between":[92],"marginals.":[94],"prove":[96],"minimization":[99],"allows":[100],"class-conditionals":[103],"given":[104],"mild":[105],"assumptions":[106],"their":[108],"geometry.":[109],"Our":[110],"experimental":[111],"results":[112],"our":[115],"method":[116],"performs":[117],"better":[118],"average":[120],"than":[121],"competitors":[122],"accross":[123],"range":[125],"problems":[128],"including":[129],"digits,VisDA":[130],"Office.":[132]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2020-06-19T00:00:00"}