{"id":"https://openalex.org/W4283065821","doi":"https://doi.org/10.1080/10618600.2022.2090946","title":"High-Dimensional Multi-Task Learning using Multivariate Regression and Generalized Fiducial Inference","display_name":"High-Dimensional Multi-Task Learning using Multivariate Regression and Generalized Fiducial Inference","publication_year":2022,"publication_date":"2022-06-17","ids":{"openalex":"https://openalex.org/W4283065821","doi":"https://doi.org/10.1080/10618600.2022.2090946"},"language":"en","primary_location":{"id":"doi:10.1080/10618600.2022.2090946","is_oa":false,"landing_page_url":"https://doi.org/10.1080/10618600.2022.2090946","pdf_url":null,"source":{"id":"https://openalex.org/S76159266","display_name":"Journal of Computational and Graphical Statistics","issn_l":"1061-8600","issn":["1061-8600","1537-2715"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547","https://openalex.org/P4310320449"],"host_organization_lineage_names":["Taylor & Francis","Informa"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computational and Graphical Statistics","raw_type":"journal-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/A5085692590","display_name":"Zhenyu Wei","orcid":null},"institutions":[{"id":"https://openalex.org/I84218800","display_name":"University of California, Davis","ror":"https://ror.org/05rrcem69","country_code":"US","type":"education","lineage":["https://openalex.org/I84218800"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhenyu Wei","raw_affiliation_strings":["Department of Statistics, University of California, Davis, Davis, CA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, University of California, Davis, Davis, CA","institution_ids":["https://openalex.org/I84218800"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049635087","display_name":"Thomas C. M. Lee","orcid":"https://orcid.org/0000-0001-7067-405X"},"institutions":[{"id":"https://openalex.org/I84218800","display_name":"University of California, Davis","ror":"https://ror.org/05rrcem69","country_code":"US","type":"education","lineage":["https://openalex.org/I84218800"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Thomas C. M. Lee","raw_affiliation_strings":["Department of Statistics, University of California, Davis, Davis, CA"],"raw_orcid":"https://orcid.org/0000-0001-7067-405X","affiliations":[{"raw_affiliation_string":"Department of Statistics, University of California, Davis, Davis, CA","institution_ids":["https://openalex.org/I84218800"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5049635087"],"corresponding_institution_ids":["https://openalex.org/I84218800"],"apc_list":null,"apc_paid":null,"fwci":0.2187,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.52126242,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":"32","issue":"1","first_page":"226","last_page":"240"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9977999925613403,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9977999925613403,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.996399998664856,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9904000163078308,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.6762925982475281},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6512939929962158},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6410107612609863},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6360741257667542},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.5857399702072144},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4740968644618988},{"id":"https://openalex.org/keywords/density-estimation","display_name":"Density estimation","score":0.4409756064414978},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.4272996187210083},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2697935402393341},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.23493146896362305}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6762925982475281},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6512939929962158},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6410107612609863},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6360741257667542},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.5857399702072144},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4740968644618988},{"id":"https://openalex.org/C189508267","wikidata":"https://www.wikidata.org/wiki/Q17088227","display_name":"Density estimation","level":3,"score":0.4409756064414978},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.4272996187210083},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2697935402393341},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.23493146896362305},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/10618600.2022.2090946","is_oa":false,"landing_page_url":"https://doi.org/10.1080/10618600.2022.2090946","pdf_url":null,"source":{"id":"https://openalex.org/S76159266","display_name":"Journal of Computational and Graphical Statistics","issn_l":"1061-8600","issn":["1061-8600","1537-2715"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547","https://openalex.org/P4310320449"],"host_organization_lineage_names":["Taylor & Francis","Informa"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computational and Graphical Statistics","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1382340671","display_name":"Collaborative Research: Multi-Scale Modeling of Non-Gaussian Random Fields","funder_award_id":"1811405","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G2045930519","display_name":"DMS-EPSRC Collaborative Research: Advancing Statistical Foundations and Frontiers for and from Emerging Astronomical Data Challenges","funder_award_id":"2113605","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"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1481857945","https://openalex.org/W1896424170","https://openalex.org/W1986650169","https://openalex.org/W1988930747","https://openalex.org/W2034230784","https://openalex.org/W2039066264","https://openalex.org/W2078194248","https://openalex.org/W2101593431","https://openalex.org/W2108288757","https://openalex.org/W2111162388","https://openalex.org/W2125242548","https://openalex.org/W2133726454","https://openalex.org/W2135046866","https://openalex.org/W2147848557","https://openalex.org/W2150721697","https://openalex.org/W2154560360","https://openalex.org/W2167958905","https://openalex.org/W2326809886","https://openalex.org/W2332695057","https://openalex.org/W2559655401","https://openalex.org/W2573934436","https://openalex.org/W2593406883","https://openalex.org/W2913340405","https://openalex.org/W2963877604","https://openalex.org/W2990138404","https://openalex.org/W4242743071","https://openalex.org/W4246875549","https://openalex.org/W4249847358","https://openalex.org/W4250764645","https://openalex.org/W4294541781","https://openalex.org/W4298870207"],"related_works":["https://openalex.org/W2406638334","https://openalex.org/W4390961098","https://openalex.org/W2055243143","https://openalex.org/W1991765889","https://openalex.org/W1990068454","https://openalex.org/W2472172556","https://openalex.org/W2280920478","https://openalex.org/W1570805059","https://openalex.org/W2357266745","https://openalex.org/W2361261277"],"abstract_inverted_index":{"Over":[0],"the":[1,4,14,47,64,82,121],"past":[2],"decades,":[3],"Multi-Task":[5],"Learning":[6],"(MTL)":[7],"problem":[8,66],"has":[9],"attracted":[10],"much":[11],"attention":[12],"in":[13,25],"artificial":[15],"intelligence":[16],"and":[17,55,70,99,127,147],"machine":[18],"learning":[19],"communities.":[20],"However,":[21],"most":[22],"published":[23],"work":[24],"this":[26,61,90,156],"area":[27],"focuses":[28],"on":[29,81,120],"point":[30,93],"estimation;":[31],"that":[32],"is,":[33],"estimating":[34],"model":[35,53],"parameters":[36],"and/or":[37],"making":[38],"predictions.":[39,56],"This":[40],"article":[41,62,157],"studies":[42],"another":[43],"important":[44],"aspect":[45],"of":[46,84,107,144],"MTL":[48,65],"problem:":[49],"uncertainty":[50],"quantification":[51],"for":[52,75,105,155],"choices":[54],"To":[57],"be":[58,103],"more":[59],"specific,":[60],"approaches":[63],"with":[67],"multivariate":[68],"regression":[69,87],"develops":[71],"a":[72,77,142],"novel":[73],"method":[74],"deriving":[76],"probability":[78],"density":[79,91],"function":[80],"space":[83],"all":[85],"potential":[86],"models.":[88],"With":[89],"function,":[92],"estimates,":[94],"as":[95,97,110],"well":[96],"confidence":[98],"prediction":[100],"ellipsoids,":[101],"can":[102],"obtained":[104],"quantities":[106],"interest,":[108],"such":[109],"future":[111],"observations.":[112],"The":[113],"proposed":[114],"method,":[115],"termed":[116],"GMTask,":[117],"is":[118,128],"based":[119],"generalized":[122],"fiducial":[123],"inference":[124],"(GFI)":[125],"framework":[126],"shown":[129],"to":[130,149],"enjoy":[131],"desirable":[132],"theoretical":[133],"properties.":[134],"Its":[135],"promising":[136],"empirical":[137],"properties":[138],"are":[139,158],"illustrated":[140],"via":[141],"sequence":[143],"numerical":[145],"experiments":[146],"applications":[148],"two":[150],"real":[151],"datasets.":[152],"Supplementary":[153],"materials":[154],"available":[159],"online.":[160]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-08-15T07:11:24.734988","created_date":"2025-10-10T00:00:00"}
