{"id":"https://openalex.org/W4225324481","doi":"https://doi.org/10.1109/icassp43922.2022.9747362","title":"Mismatched Supervised Learning","display_name":"Mismatched Supervised Learning","publication_year":2022,"publication_date":"2022-04-27","ids":{"openalex":"https://openalex.org/W4225324481","doi":"https://doi.org/10.1109/icassp43922.2022.9747362"},"language":"en","primary_location":{"id":"doi:10.1109/icassp43922.2022.9747362","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp43922.2022.9747362","pdf_url":null,"source":{"id":"https://openalex.org/S4363607702","display_name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5043161215","display_name":"Xun Xian","orcid":null},"institutions":[{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xun Xian","raw_affiliation_strings":["University of Minnesota,Department of Electrical and Computer Engineering","Department of Electrical and Computer Engineering, University of Minnesota"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Minnesota,Department of Electrical and Computer Engineering","institution_ids":["https://openalex.org/I130238516"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Minnesota","institution_ids":["https://openalex.org/I130238516"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100633783","display_name":"Mingyi Hong","orcid":"https://orcid.org/0000-0003-1263-9365"},"institutions":[{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mingyi Hong","raw_affiliation_strings":["University of Minnesota,Department of Electrical and Computer Engineering","Department of Electrical and Computer Engineering, University of Minnesota"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Minnesota,Department of Electrical and Computer Engineering","institution_ids":["https://openalex.org/I130238516"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Minnesota","institution_ids":["https://openalex.org/I130238516"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069838077","display_name":"Jie Ding","orcid":"https://orcid.org/0000-0002-3584-6140"},"institutions":[{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jie Ding","raw_affiliation_strings":["School of Statistics, University of Minnesota"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Statistics, University of Minnesota","institution_ids":["https://openalex.org/I130238516"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I130238516"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"26","issue":null,"first_page":"4228","last_page":"4232"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":0.9925000071525574,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":0.9925000071525574,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9919999837875366,"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/T11106","display_name":"Data Management and Algorithms","score":0.9918000102043152,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.8372362852096558},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.7436710000038147},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.7113431692123413},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6122804284095764},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5156570672988892},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.504238486289978},{"id":"https://openalex.org/keywords/identifier","display_name":"Identifier","score":0.47276097536087036},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3752315044403076},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.1303575038909912}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8372362852096558},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.7436710000038147},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7113431692123413},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6122804284095764},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5156570672988892},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.504238486289978},{"id":"https://openalex.org/C154504017","wikidata":"https://www.wikidata.org/wiki/Q853614","display_name":"Identifier","level":2,"score":0.47276097536087036},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3752315044403076},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.1303575038909912},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp43922.2022.9747362","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp43922.2022.9747362","pdf_url":null,"source":{"id":"https://openalex.org/S4363607702","display_name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W111418871","https://openalex.org/W2003447360","https://openalex.org/W2004886822","https://openalex.org/W2102201073","https://openalex.org/W2151417896","https://openalex.org/W2158131535","https://openalex.org/W2396881363","https://openalex.org/W2513506629","https://openalex.org/W2610882944","https://openalex.org/W2617602334","https://openalex.org/W2744999500","https://openalex.org/W2912213068","https://openalex.org/W2963518783","https://openalex.org/W2963572345","https://openalex.org/W2963755373","https://openalex.org/W3007679270","https://openalex.org/W3010923953","https://openalex.org/W3038679613","https://openalex.org/W3080183383","https://openalex.org/W3096904619","https://openalex.org/W3101973032","https://openalex.org/W3103896557","https://openalex.org/W3129625272","https://openalex.org/W4233762729","https://openalex.org/W4250955649","https://openalex.org/W6604484970","https://openalex.org/W6682962330","https://openalex.org/W6736735008","https://openalex.org/W6738279660","https://openalex.org/W6743073161","https://openalex.org/W6765987612","https://openalex.org/W6780274001","https://openalex.org/W6785528530","https://openalex.org/W6786114458"],"related_works":["https://openalex.org/W2390777183","https://openalex.org/W2461970972","https://openalex.org/W1882848237","https://openalex.org/W2364921833","https://openalex.org/W2388030554","https://openalex.org/W2302028273","https://openalex.org/W1525643724","https://openalex.org/W2385763152","https://openalex.org/W2375463041","https://openalex.org/W2177394719"],"abstract_inverted_index":{"Supervised":[0],"learning":[1,17,51],"scenarios,":[2],"where":[3],"labels":[4],"and":[5,76,87,108,118,126,132],"features":[6],"are":[7,70,81],"possibly":[8],"mismatched,":[9],"have":[10],"been":[11],"an":[12],"emerging":[13],"concern":[14],"in":[15,39],"machine":[16],"applications.":[18],"For":[19],"example,":[20],"researchers":[21],"often":[22,71],"need":[23],"to":[24,31,60,84,122],"align":[25],"heterogeneous":[26],"data":[27,125,134],"from":[28],"multiple":[29],"resources":[30],"the":[32,40,50,61,65,100,140],"same":[33],"entities":[34],"without":[35],"a":[36,44,96,116],"unique":[37],"identifier":[38],"socioeconomic":[41],"study.":[42],"Such":[43],"mismatch":[45,66,101],"problem":[46,102],"can":[47],"significantly":[48,136],"affect":[49],"performance":[52,138],"if":[53],"it":[54],"is":[55],"not":[56,82],"appropriately":[57],"addressed.":[58],"Due":[59],"combinatorial":[62],"nature":[63],"of":[64,99,139],"problem,":[67],"existing":[68],"methods":[69],"designed":[72],"for":[73,110],"small":[74],"datasets":[75,86],"simple":[77],"linear":[78],"models":[79],"but":[80],"scalable":[83],"large-scale":[85],"complex":[88,124],"models.":[89,127],"In":[90],"this":[91],"paper,":[92],"we":[93,114],"first":[94],"present":[95],"new":[97],"formulation":[98],"that":[103],"supports":[104],"continuous":[105],"optimization":[106],"problems":[107],"allows":[109],"gradient-based":[111],"methods.":[112,145],"Moreover,":[113],"develop":[115],"computation":[117],"memory":[119],"efficient":[120],"method":[121],"process":[123],"Empirical":[128],"studies":[129],"on":[130],"synthetic":[131],"real-world":[133],"show":[135],"better":[137],"proposed":[141],"algorithms":[142],"than":[143],"state-of-the-art":[144]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
