{"id":"https://openalex.org/W2104094955","doi":"https://doi.org/10.1007/s10994-009-5152-4","title":"A theory of learning from different domains","display_name":"A theory of learning from different domains","publication_year":2009,"publication_date":"2009-10-22","ids":{"openalex":"https://openalex.org/W2104094955","doi":"https://doi.org/10.1007/s10994-009-5152-4","mag":"2104094955"},"language":"en","primary_location":{"id":"doi:10.1007/s10994-009-5152-4","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-009-5152-4","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-009-5152-4.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s10994-009-5152-4.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5112193907","display_name":"Shai Ben-David","orcid":null},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Shai Ben-David","raw_affiliation_strings":["David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, Canada","David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Canada"],"affiliations":[{"raw_affiliation_string":"David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, Canada","institution_ids":["https://openalex.org/I151746483"]},{"raw_affiliation_string":"David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Canada","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053208168","display_name":"John Blitzer","orcid":null},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"John Blitzer","raw_affiliation_strings":["Department of Computer Science, UC Berkeley, Berkeley, CA, USA","Dept. of Computer Science, UC Berkeley, Berkeley, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, UC Berkeley, Berkeley, CA, USA","institution_ids":["https://openalex.org/I95457486"]},{"raw_affiliation_string":"Dept. of Computer Science, UC Berkeley, Berkeley, USA","institution_ids":["https://openalex.org/I95457486"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006419939","display_name":"Koby Crammer","orcid":"https://orcid.org/0000-0001-8824-5747"},"institutions":[{"id":"https://openalex.org/I174306211","display_name":"Technion \u2013 Israel Institute of Technology","ror":"https://ror.org/03qryx823","country_code":"IL","type":"education","lineage":["https://openalex.org/I174306211"]}],"countries":["IL"],"is_corresponding":false,"raw_author_name":"Koby Crammer","raw_affiliation_strings":["Department of Electrical Engineering, The Technion, Haifa, Israel"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, The Technion, Haifa, Israel","institution_ids":["https://openalex.org/I174306211"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032936063","display_name":"Alex Kulesza","orcid":null},"institutions":[{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alex Kulesza","raw_affiliation_strings":["Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA","Department of Computer and Information Science, University of Pennsylvania, Philadelphia, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA","institution_ids":["https://openalex.org/I79576946"]},{"raw_affiliation_string":"Department of Computer and Information Science, University of Pennsylvania, Philadelphia, USA","institution_ids":["https://openalex.org/I79576946"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044708805","display_name":"Fernando Pereira","orcid":"https://orcid.org/0000-0001-6100-947X"},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fernando Pereira","raw_affiliation_strings":["Google Research, Mountain View, CA, USA","Google Research, Mountain View, USA#TAB#"],"affiliations":[{"raw_affiliation_string":"Google Research, Mountain View, CA, USA","institution_ids":["https://openalex.org/I1291425158"]},{"raw_affiliation_string":"Google Research, Mountain View, USA#TAB#","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5043117896","display_name":"Jennifer Wortman Vaughan","orcid":"https://orcid.org/0000-0002-7807-2018"},"institutions":[{"id":"https://openalex.org/I136199984","display_name":"Harvard University","ror":"https://ror.org/03vek6s52","country_code":"US","type":"education","lineage":["https://openalex.org/I136199984"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jennifer Wortman Vaughan","raw_affiliation_strings":["School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA","School of Engineering & Applied Sciences, Harvard University, Cambridge, USA"],"affiliations":[{"raw_affiliation_string":"School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA","institution_ids":["https://openalex.org/I136199984"]},{"raw_affiliation_string":"School of Engineering & Applied Sciences, Harvard University, Cambridge, USA","institution_ids":["https://openalex.org/I136199984"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5112193907"],"corresponding_institution_ids":["https://openalex.org/I151746483"],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":25.7494,"has_fulltext":true,"cited_by_count":3401,"citation_normalized_percentile":{"value":0.9965378,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"79","issue":"1-2","first_page":"151","last_page":"175"},"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":0.9991999864578247,"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":0.9991999864578247,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.998199999332428,"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":0.9965999722480774,"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/classifier","display_name":"Classifier (UML)","score":0.8017970323562622},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6685967445373535},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.6184406876564026},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.5630748271942139},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5499082803726196},{"id":"https://openalex.org/keywords/bounding-overwatch","display_name":"Bounding overwatch","score":0.530665934085846},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5023400783538818},{"id":"https://openalex.org/keywords/bayes-error-rate","display_name":"Bayes error rate","score":0.48295944929122925},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.44085124135017395},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.42327970266342163},{"id":"https://openalex.org/keywords/test-data","display_name":"Test data","score":0.41937321424484253},{"id":"https://openalex.org/keywords/quadratic-classifier","display_name":"Quadratic classifier","score":0.418178915977478},{"id":"https://openalex.org/keywords/divergence","display_name":"Divergence (linguistics)","score":0.41345149278640747},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3966059684753418},{"id":"https://openalex.org/keywords/bayes-classifier","display_name":"Bayes classifier","score":0.25460273027420044},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.19258546829223633},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.13273847103118896}],"concepts":[{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.8017970323562622},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6685967445373535},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.6184406876564026},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.5630748271942139},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5499082803726196},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.530665934085846},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5023400783538818},{"id":"https://openalex.org/C143809311","wikidata":"https://www.wikidata.org/wiki/Q4874458","display_name":"Bayes error rate","level":5,"score":0.48295944929122925},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.44085124135017395},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42327970266342163},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.41937321424484253},{"id":"https://openalex.org/C52620605","wikidata":"https://www.wikidata.org/wiki/Q7268357","display_name":"Quadratic classifier","level":3,"score":0.418178915977478},{"id":"https://openalex.org/C207390915","wikidata":"https://www.wikidata.org/wiki/Q1230525","display_name":"Divergence (linguistics)","level":2,"score":0.41345149278640747},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3966059684753418},{"id":"https://openalex.org/C185207860","wikidata":"https://www.wikidata.org/wiki/Q17004744","display_name":"Bayes classifier","level":4,"score":0.25460273027420044},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.19258546829223633},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.13273847103118896},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1007/s10994-009-5152-4","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-009-5152-4","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-009-5152-4.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning","raw_type":"journal-article"},{"id":"pmh:oai:escholarship.org/ark:/13030/qt2nv1j9sc","is_oa":false,"landing_page_url":"https://escholarship.org/uc/item/2nv1j9sc","pdf_url":null,"source":{"id":"https://openalex.org/S4306400115","display_name":"eScholarship (California Digital Library)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2801248553","host_organization_name":"California Digital Library","host_organization_lineage":["https://openalex.org/I2801248553"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Machine Learning, vol 79, iss 1","raw_type":"article"},{"id":"pmh:qt2nv1j9sc","is_oa":false,"landing_page_url":"http://www.escholarship.org/uc/item/2nv1j9sc","pdf_url":null,"source":{"id":"https://openalex.org/S4306400115","display_name":"eScholarship (California Digital Library)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2801248553","host_organization_name":"California Digital Library","host_organization_lineage":["https://openalex.org/I2801248553"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Ben-David, Shai; Blitzer, John; Crammer, Koby; Kulesza, Alex; Pereira, Fernando; &amp; Vaughan, Jennifer Wortman. (2010). A theory of learning from different domains. Machine Learning, 79(1), pp 151-175. doi: 10.1007/s10994-009-5152-4. Retrieved from: http://www.escholarship.org/uc/item/2nv1j9sc","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1007/s10994-009-5152-4","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-009-5152-4","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-009-5152-4.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.7400000095367432,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G2857030524","display_name":null,"funder_award_id":"NBCHD030010","funder_id":"https://openalex.org/F4320332180","funder_display_name":"Defense Advanced Research Projects Agency"},{"id":"https://openalex.org/G3999179517","display_name":null,"funder_award_id":"NBCHD03001","funder_id":"https://openalex.org/F4320332180","funder_display_name":"Defense Advanced Research Projects Agency"},{"id":"https://openalex.org/G6783887022","display_name":"RI-Medium: Collaborative Research: Dynamically-Structured Conditional Random Fields for Complex, Natural Domains","funder_award_id":"0803256","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8442137556","display_name":"ITR:     Collaborative Research:    (ACS+NHS)-(dmc+soc):    Machine Learning for Sequences and Structured Data:    Tools for Non-Experts","funder_award_id":"0428193","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G848032724","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320309370","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10"},{"id":"https://openalex.org/F4320332180","display_name":"Defense Advanced Research Projects Agency","ror":"https://ror.org/02caytj08"},{"id":"https://openalex.org/F4320332815","display_name":"Advanced Research Projects Agency","ror":"https://ror.org/02caytj08"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2104094955.pdf","grobid_xml":"https://content.openalex.org/works/W2104094955.grobid-xml"},"referenced_works_count":44,"referenced_works":["https://openalex.org/W22024230","https://openalex.org/W1542886316","https://openalex.org/W1607832978","https://openalex.org/W1608944489","https://openalex.org/W1773803948","https://openalex.org/W1853837125","https://openalex.org/W1966026565","https://openalex.org/W1967807490","https://openalex.org/W1968934255","https://openalex.org/W2062291443","https://openalex.org/W2091825929","https://openalex.org/W2092654472","https://openalex.org/W2104936489","https://openalex.org/W2105523772","https://openalex.org/W2110091014","https://openalex.org/W2111362445","https://openalex.org/W2112483442","https://openalex.org/W2120354757","https://openalex.org/W2120587290","https://openalex.org/W2122838776","https://openalex.org/W2130903752","https://openalex.org/W2131953535","https://openalex.org/W2134169350","https://openalex.org/W2139122730","https://openalex.org/W2148440006","https://openalex.org/W2148603752","https://openalex.org/W2162888803","https://openalex.org/W2163302275","https://openalex.org/W2163918411","https://openalex.org/W2166706824","https://openalex.org/W2579923771","https://openalex.org/W2949998441","https://openalex.org/W2951278869","https://openalex.org/W3104240813","https://openalex.org/W3146306708","https://openalex.org/W4249716558","https://openalex.org/W6637852806","https://openalex.org/W6675865240","https://openalex.org/W6676587327","https://openalex.org/W6676840641","https://openalex.org/W6679554340","https://openalex.org/W6680880821","https://openalex.org/W6793289451","https://openalex.org/W7071374342"],"related_works":["https://openalex.org/W2115065944","https://openalex.org/W58702947","https://openalex.org/W2187639235","https://openalex.org/W2060931694","https://openalex.org/W92531827","https://openalex.org/W4287241967","https://openalex.org/W3144173820","https://openalex.org/W1801413419","https://openalex.org/W1849414697","https://openalex.org/W1622451593"],"abstract_inverted_index":{"Discriminative":[0],"learning":[1],"methods":[2],"for":[3],"classification":[4],"perform":[5,75],"well":[6,38,76,164],"when":[7],"training":[8,24,52,95],"and":[9,47,130,209,244,259,274,297],"test":[10,111],"data":[11,25,71,103],"are":[12],"drawn":[13],"from":[14,26,69,147,151,229],"the":[15,97,106,115,131,134,152,155,175,180,189,194,206,219,223,230,239,251,253,260,263,270,288],"same":[16],"distribution.":[17],"Often,":[18],"however,":[19],"we":[20,57,91,168],"have":[21],"plentiful":[22],"labeled":[23,51,86,101],"a":[27,34,40,44,66,82,120,139,184,198,202,248,282],"source":[28,70,102,128,177,208,220,243,296],"domain":[29,42],"but":[30],"wish":[31],"to":[32,74,104,237],"learn":[33],"classifier":[35,67],"which":[36,200,284],"performs":[37,163],"on":[39,77],"target":[41,78,87,108,122,181,195,210,224,245,289,298],"with":[43,96,174],"different":[45],"distribution":[46],"little":[48],"or":[49,226,291],"no":[50],"data.":[53],"In":[54],"this":[55,171],"work":[56,214],"investigate":[58],"two":[59,135,231],"questions.":[60],"First,":[61],"under":[62],"what":[63],"conditions":[64],"can":[65,144],"trained":[68],"be":[72,145],"expected":[73],"data?":[79],"Second,":[80],"given":[81],"small":[83],"amount":[84,99],"of":[85,100,126,183,197,205,242,250,256,262,295],"data,":[88],"how":[89,236],"should":[90],"combine":[92],"it":[93],"during":[94],"large":[98],"achieve":[105],"lowest":[107],"error":[109,123,129,178,182,196,246,290],"at":[110,277],"time?":[112],"We":[113,137,187,234],"address":[114],"first":[116],"question":[117,191],"by":[118,192],"bounding":[119,193],"classifier\u2019s":[121],"in":[124,165],"terms":[125],"its":[127],"divergence":[132,141],"between":[133],"domains.":[136,153],"give":[138],"classifier-induced":[140],"measure":[142],"that":[143,157,162,170],"estimated":[146],"finite,":[148],"unlabeled":[149],"samples":[150],"Under":[154],"assumption":[156],"there":[158],"exists":[159],"some":[160],"hypothesis":[161,264],"both":[166,257],"domains,":[167,258],"show":[169,235],"quantity":[172],"together":[173],"empirical":[176,207],"characterize":[179],"source-trained":[185],"classifier.":[186],"answer":[188],"second":[190],"model":[199],"minimizes":[201],"convex":[203],"combination":[204,241],"errors.":[211,299],"Previous":[212],"theoretical":[213],"has":[215],"considered":[216],"minimizing":[217,286],"just":[218,222],"error,":[221,225],"weighting":[227,294],"instances":[228],"domains":[232],"equally.":[233],"choose":[238],"optimal":[240],"as":[247,279,281],"function":[249],"divergence,":[252],"sample":[254],"sizes":[255],"complexity":[261],"class.":[265],"The":[266],"resulting":[267],"bound":[268,283],"generalizes":[269],"previously":[271],"studied":[272],"cases":[273],"is":[275],"always":[276],"least":[278],"tight":[280],"considers":[285],"only":[287],"an":[292],"equal":[293]},"counts_by_year":[{"year":2026,"cited_by_count":89},{"year":2025,"cited_by_count":358},{"year":2024,"cited_by_count":386},{"year":2023,"cited_by_count":422},{"year":2022,"cited_by_count":371},{"year":2021,"cited_by_count":540},{"year":2020,"cited_by_count":480},{"year":2019,"cited_by_count":280},{"year":2018,"cited_by_count":139},{"year":2017,"cited_by_count":90},{"year":2016,"cited_by_count":67},{"year":2015,"cited_by_count":41},{"year":2014,"cited_by_count":39},{"year":2013,"cited_by_count":42},{"year":2012,"cited_by_count":30}],"updated_date":"2026-04-14T08:04:32.555800","created_date":"2025-10-10T00:00:00"}
