{"id":"https://openalex.org/W3017305020","doi":"https://doi.org/10.1109/tpami.2020.2987482","title":"Minimizing Negative Transfer of Knowledge in Multivariate Gaussian Processes: A Scalable and Regularized Approach","display_name":"Minimizing Negative Transfer of Knowledge in Multivariate Gaussian Processes: A Scalable and Regularized Approach","publication_year":2020,"publication_date":"2020-04-15","ids":{"openalex":"https://openalex.org/W3017305020","doi":"https://doi.org/10.1109/tpami.2020.2987482","mag":"3017305020","pmid":"https://pubmed.ncbi.nlm.nih.gov/32305903"},"language":"en","primary_location":{"id":"doi:10.1109/tpami.2020.2987482","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2020.2987482","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5075324117","display_name":"Raed Al Kontar","orcid":"https://orcid.org/0000-0002-4546-324X"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Raed Kontar","raw_affiliation_strings":["Department of Industrial & Operations Engineering Engineering, Universty of Michigan, Ann Arbor, MI, USA"],"raw_orcid":"https://orcid.org/0000-0002-4546-324X","affiliations":[{"raw_affiliation_string":"Department of Industrial & Operations Engineering Engineering, Universty of Michigan, Ann Arbor, MI, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064890839","display_name":"Garvesh Raskutti","orcid":"https://orcid.org/0000-0002-0522-5423"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Garvesh Raskutti","raw_affiliation_strings":["Universty of Wisconsin Madison, Madison, WI, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universty of Wisconsin Madison, Madison, WI, USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000499123","display_name":"Shiyu Zhou","orcid":"https://orcid.org/0000-0002-5902-8812"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shiyu Zhou","raw_affiliation_strings":["Universty of Wisconsin Madison, Madison, WI, USA"],"raw_orcid":"https://orcid.org/0000-0002-5902-8812","affiliations":[{"raw_affiliation_string":"Universty of Wisconsin Madison, Madison, WI, USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5075324117"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.9896,"has_fulltext":false,"cited_by_count":33,"citation_normalized_percentile":{"value":0.92803629,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"43","issue":"10","first_page":"3508","last_page":"3522"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9994000196456909,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9994000196456909,"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/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.9922000169754028,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9790999889373779,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6715340614318848},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.6138550043106079},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.5940380096435547},{"id":"https://openalex.org/keywords/bivariate-analysis","display_name":"Bivariate analysis","score":0.5504060387611389},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5273481607437134},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.4869007170200348},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.4467565715312958},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.43202993273735046},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.40016525983810425},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3981330990791321},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.394572913646698},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3653401732444763},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3615851104259491}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6715340614318848},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.6138550043106079},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.5940380096435547},{"id":"https://openalex.org/C64341305","wikidata":"https://www.wikidata.org/wiki/Q4919225","display_name":"Bivariate analysis","level":2,"score":0.5504060387611389},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5273481607437134},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4869007170200348},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.4467565715312958},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.43202993273735046},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.40016525983810425},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3981330990791321},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.394572913646698},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3653401732444763},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3615851104259491},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tpami.2020.2987482","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2020.2987482","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},{"id":"pmid:32305903","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/32305903","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on pattern analysis and machine intelligence","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5030409466","display_name":null,"funder_award_id":"#1561512","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6281930588","display_name":null,"funder_award_id":"#1811767","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":80,"referenced_works":["https://openalex.org/W34929345","https://openalex.org/W110304591","https://openalex.org/W133251416","https://openalex.org/W170782846","https://openalex.org/W241901432","https://openalex.org/W1480376833","https://openalex.org/W1502922572","https://openalex.org/W1510085384","https://openalex.org/W1534349507","https://openalex.org/W1571870753","https://openalex.org/W1585754671","https://openalex.org/W1599057079","https://openalex.org/W1911299338","https://openalex.org/W1964490787","https://openalex.org/W1973310094","https://openalex.org/W1978056375","https://openalex.org/W1995793834","https://openalex.org/W1996827138","https://openalex.org/W2005850290","https://openalex.org/W2016028169","https://openalex.org/W2026757872","https://openalex.org/W2040932457","https://openalex.org/W2042504105","https://openalex.org/W2061572659","https://openalex.org/W2067042195","https://openalex.org/W2074682976","https://openalex.org/W2078454401","https://openalex.org/W2079724595","https://openalex.org/W2094009285","https://openalex.org/W2108966602","https://openalex.org/W2113325425","https://openalex.org/W2123899739","https://openalex.org/W2125650133","https://openalex.org/W2134894576","https://openalex.org/W2143672530","https://openalex.org/W2146611938","https://openalex.org/W2148124601","https://openalex.org/W2153347097","https://openalex.org/W2153675946","https://openalex.org/W2155162055","https://openalex.org/W2156614992","https://openalex.org/W2165698076","https://openalex.org/W2167503371","https://openalex.org/W2167826316","https://openalex.org/W2167986580","https://openalex.org/W2244749007","https://openalex.org/W2259188558","https://openalex.org/W2314945980","https://openalex.org/W2531559099","https://openalex.org/W2597248468","https://openalex.org/W2749838743","https://openalex.org/W2752783222","https://openalex.org/W2756837310","https://openalex.org/W2800674339","https://openalex.org/W2914746235","https://openalex.org/W2949536823","https://openalex.org/W3022833386","https://openalex.org/W4205685297","https://openalex.org/W4206030159","https://openalex.org/W4206212643","https://openalex.org/W4233406868","https://openalex.org/W4239943352","https://openalex.org/W4255521522","https://openalex.org/W4293579573","https://openalex.org/W4301253241","https://openalex.org/W6601447099","https://openalex.org/W6604527594","https://openalex.org/W6605391468","https://openalex.org/W6609413351","https://openalex.org/W6630533240","https://openalex.org/W6640000427","https://openalex.org/W6676276870","https://openalex.org/W6678713070","https://openalex.org/W6681154564","https://openalex.org/W6681929560","https://openalex.org/W6682399247","https://openalex.org/W6684921082","https://openalex.org/W6690827749","https://openalex.org/W6691831079","https://openalex.org/W6744579343"],"related_works":["https://openalex.org/W2487162673","https://openalex.org/W2949152769","https://openalex.org/W4372354731","https://openalex.org/W2562400057","https://openalex.org/W1692008701","https://openalex.org/W1527023294","https://openalex.org/W4387164954","https://openalex.org/W2142494763","https://openalex.org/W2169494453","https://openalex.org/W2056895937"],"abstract_inverted_index":{"Recently":[0],"there":[1],"has":[2,212],"been":[3],"an":[4],"increasing":[5],"interest":[6],"in":[7,91,190],"the":[8,15,28,47,57,60,88,102,131,145,148,176,183,202,216,224,235],"multivariate":[9,150],"Gaussian":[10,16],"process":[11,17,40],"(MGP)":[12],"which":[13,158,171],"extends":[14],"(GP)":[18],"to":[19,26,70,87,98,143,167,174],"deal":[20],"with":[21,75],"multiple":[22],"outputs.":[23,231],"One":[24],"approach":[25,129,141],"construct":[27],"MGP":[29,132],"and":[30,94,222,240],"account":[31],"for":[32,130,234],"non-trivial":[33],"commonalities":[34],"amongst":[35],"outputs":[36,111,219],"employs":[37],"a":[38,76,125,153,206],"convolution":[39],"(CP).":[41],"The":[42,136,209],"CP":[43,61],"is":[44,83,142,220],"based":[45],"on":[46],"idea":[48],"of":[49,59,79,96,105,139,147,155,178,218,227],"sharing":[50,189],"latent":[51,184],"functions":[52],"across":[53],"several":[54],"convolutions.":[55],"Despite":[56],"elegance":[58],"construction,":[62],"it":[63],"provides":[64],"new":[65],"challenges":[66],"that":[67,186],"need":[68],"yet":[69],"be":[71,99],"tackled.":[72],"First,":[73],"even":[74],"moderate":[77],"number":[78,95,217],"outputs,":[80],"model":[81,151],"building":[82],"extremely":[84],"prohibitive":[85],"due":[86],"huge":[89],"increase":[90],"computational":[92],"demands":[93],"parameters":[97],"estimated.":[100],"Second,":[101],"negative":[103,179,225],"transfer":[104,180,226],"knowledge":[106,228],"may":[107],"occur":[108],"when":[109,215],"some":[110],"do":[112],"not":[113],"share":[114],"commonalities.":[115],"In":[116],"this":[117],"paper":[118],"we":[119],"address":[120],"these":[121],"issues.":[122],"We":[123],"propose":[124],"regularized":[126],"pairwise":[127,163],"modeling":[128,164],"established":[133],"using":[134],"CP.":[135],"key":[137],"feature":[138],"our":[140],"distribute":[144],"estimation":[146],"full":[149],"into":[152],"group":[154],"bivariate":[156,192,203],"GPs":[157],"are":[159,195,238,244],"individually":[160],"built.":[161],"Interestingly":[162],"turns":[165],"out":[166],"possess":[168],"unique":[169],"characteristics,":[170],"allows":[172],"us":[173],"tackle":[175],"challenge":[177],"through":[181,198,246],"penalizing":[182],"function":[185],"facilitates":[187],"information":[188],"each":[191],"model.":[193],"Predictions":[194],"then":[196],"made":[197],"combining":[199],"predictions":[200],"from":[201],"models":[204],"within":[205],"Bayesian":[207],"framework.":[208],"proposed":[210,236],"method":[211,237],"excellent":[213],"scalability":[214],"large":[221],"minimizes":[223],"between":[229],"uncorrelated":[230],"Statistical":[232],"guarantees":[233],"studied":[239],"its":[241],"advantageous":[242],"features":[243],"demonstrated":[245],"numerical":[247],"studies.":[248]},"counts_by_year":[{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}