{"id":"https://openalex.org/W3034421838","doi":"https://doi.org/10.24963/ijcai.2020/340","title":"Scalable Gaussian Process Regression Networks","display_name":"Scalable Gaussian Process Regression Networks","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3034421838","doi":"https://doi.org/10.24963/ijcai.2020/340","mag":"3034421838"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2020/340","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2020/340","pdf_url":"https://www.ijcai.org/proceedings/2020/0340.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.ijcai.org/proceedings/2020/0340.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100669328","display_name":"Shibo Li","orcid":"https://orcid.org/0000-0001-8398-4102"},"institutions":[{"id":"https://openalex.org/I223532165","display_name":"University of Utah","ror":"https://ror.org/03r0ha626","country_code":"US","type":"education","lineage":["https://openalex.org/I223532165"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shibo Li","raw_affiliation_strings":["University of Utah","School of Computing, University of Utah"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Utah","institution_ids":["https://openalex.org/I223532165"]},{"raw_affiliation_string":"School of Computing, University of Utah","institution_ids":["https://openalex.org/I223532165"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020806445","display_name":"Wei Xing","orcid":"https://orcid.org/0000-0002-3177-8478"},"institutions":[{"id":"https://openalex.org/I223532165","display_name":"University of Utah","ror":"https://ror.org/03r0ha626","country_code":"US","type":"education","lineage":["https://openalex.org/I223532165"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wei Xing","raw_affiliation_strings":["University of Utah","Scientific Computing and Imaging Institute, University of Utah"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Utah","institution_ids":["https://openalex.org/I223532165"]},{"raw_affiliation_string":"Scientific Computing and Imaging Institute, University of Utah","institution_ids":["https://openalex.org/I223532165"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103752748","display_name":"Robert M. Kirby","orcid":null},"institutions":[{"id":"https://openalex.org/I223532165","display_name":"University of Utah","ror":"https://ror.org/03r0ha626","country_code":"US","type":"education","lineage":["https://openalex.org/I223532165"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Robert M. Kirby","raw_affiliation_strings":["University of Utah","School of Computing, University of Utah","Scientific Computing and Imaging Institute, University of Utah"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Utah","institution_ids":["https://openalex.org/I223532165"]},{"raw_affiliation_string":"School of Computing, University of Utah","institution_ids":["https://openalex.org/I223532165"]},{"raw_affiliation_string":"Scientific Computing and Imaging Institute, University of Utah","institution_ids":["https://openalex.org/I223532165"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5024663093","display_name":"Shandian Zhe","orcid":"https://orcid.org/0000-0003-0316-9875"},"institutions":[{"id":"https://openalex.org/I223532165","display_name":"University of Utah","ror":"https://ror.org/03r0ha626","country_code":"US","type":"education","lineage":["https://openalex.org/I223532165"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shandian Zhe","raw_affiliation_strings":["University of Utah","School of Computing, University of Utah"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Utah","institution_ids":["https://openalex.org/I223532165"]},{"raw_affiliation_string":"School of Computing, University of Utah","institution_ids":["https://openalex.org/I223532165"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I223532165"],"apc_list":null,"apc_paid":null,"fwci":0.23,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.47275391,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"2456","last_page":"2462"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9994999766349792,"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":0.9994999766349792,"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":0.9760000109672546,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9409999847412109,"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/computer-science","display_name":"Computer science","score":0.7068807482719421},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.624431312084198},{"id":"https://openalex.org/keywords/kronecker-product","display_name":"Kronecker product","score":0.6127921938896179},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6085208654403687},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5563626289367676},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.48689714074134827},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.47745421528816223},{"id":"https://openalex.org/keywords/posterior-probability","display_name":"Posterior probability","score":0.4455839693546295},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.44093117117881775},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.4223382771015167},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.39572468400001526},{"id":"https://openalex.org/keywords/kronecker-delta","display_name":"Kronecker delta","score":0.3717193007469177},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.32759857177734375},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3047555387020111},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.29583510756492615},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.23056071996688843}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7068807482719421},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.624431312084198},{"id":"https://openalex.org/C46030957","wikidata":"https://www.wikidata.org/wiki/Q1238125","display_name":"Kronecker product","level":3,"score":0.6127921938896179},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6085208654403687},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5563626289367676},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.48689714074134827},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.47745421528816223},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.4455839693546295},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.44093117117881775},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4223382771015167},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.39572468400001526},{"id":"https://openalex.org/C39482219","wikidata":"https://www.wikidata.org/wiki/Q192826","display_name":"Kronecker delta","level":2,"score":0.3717193007469177},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.32759857177734375},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3047555387020111},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.29583510756492615},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.23056071996688843},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2020/340","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2020/340","pdf_url":"https://www.ijcai.org/proceedings/2020/0340.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2020/340","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2020/340","pdf_url":"https://www.ijcai.org/proceedings/2020/0340.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8378494134","display_name":null,"funder_award_id":"HR0011-17-2-0016","funder_id":"https://openalex.org/F4320332180","funder_display_name":"Defense Advanced Research Projects Agency"},{"id":"https://openalex.org/G8899104502","display_name":"III: Small: Collaborative Research: Scalable Deep Bayesian Tensor Decomposition","funder_award_id":"1910983","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/F4320332180","display_name":"Defense Advanced Research Projects Agency","ror":"https://ror.org/02caytj08"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3034421838.pdf","grobid_xml":"https://content.openalex.org/works/W3034421838.grobid-xml"},"referenced_works_count":28,"referenced_works":["https://openalex.org/W36503211","https://openalex.org/W1519342765","https://openalex.org/W1522301498","https://openalex.org/W1534349507","https://openalex.org/W1545951971","https://openalex.org/W1585754671","https://openalex.org/W1746819321","https://openalex.org/W2050365444","https://openalex.org/W2076077791","https://openalex.org/W2078454401","https://openalex.org/W2093402979","https://openalex.org/W2119595900","https://openalex.org/W2120340025","https://openalex.org/W2134894576","https://openalex.org/W2139259843","https://openalex.org/W2140095548","https://openalex.org/W2141377530","https://openalex.org/W2143672530","https://openalex.org/W2149775970","https://openalex.org/W2158170196","https://openalex.org/W2167986580","https://openalex.org/W2510161586","https://openalex.org/W2921400564","https://openalex.org/W2953384591","https://openalex.org/W2984776367","https://openalex.org/W4211049957","https://openalex.org/W4230031267","https://openalex.org/W4289417766"],"related_works":["https://openalex.org/W123417539","https://openalex.org/W2079407403","https://openalex.org/W2060299328","https://openalex.org/W4375957393","https://openalex.org/W4233239985","https://openalex.org/W2117336295","https://openalex.org/W3102722572","https://openalex.org/W2090103374","https://openalex.org/W577870507","https://openalex.org/W2073634779"],"abstract_inverted_index":{"Gaussian":[0],"process":[1],"regression":[2],"networks":[3],"(GPRN)":[4],"are":[5,69],"powerful":[6],"Bayesian":[7],"models":[8],"for":[9,41,76,93,109],"multi-output":[10],"regression,":[11],"but":[12,103],"their":[13],"inference":[14,59,91],"is":[15,105],"intractable.":[16],"To":[17,82],"address":[18],"this":[19],"issue,":[20],"existing":[21],"methods":[22],"use":[23],"a":[24,29,77,88],"fully":[25],"factorized":[26],"structure":[27,144],"(or":[28],"mixture":[30],"of":[31,65,80,160],"such":[32],"structures)":[33],"over":[34],"all":[35,135],"the":[36,48,53,58,63,66,99,114,124,130,136,140,146,154,158],"outputs":[37],"and":[38,56,71,117,127,138],"latent":[39,54],"functions":[40],"posterior":[42,50,101,125],"approximation,":[43],"which,":[44],"however,":[45],"can":[46,72],"miss":[47],"strong":[49],"dependencies":[51,102],"among":[52],"variables":[55],"hurt":[57],"quality.":[60],"In":[61],"addition,":[62],"updates":[64],"variational":[67,90,120,147],"parameters":[68,137],"inefficient":[70],"be":[73],"prohibitively":[74],"expensive":[75],"large":[78],"number":[79],"outputs.":[81,111],"overcome":[83],"these":[84],"limitations,":[85],"we":[86],"propose":[87],"scalable":[89],"algorithm":[92],"GPRN,":[94],"which":[95],"not":[96],"only":[97],"captures":[98],"abundant":[100],"also":[104],"much":[106],"more":[107],"efficient":[108],"massive":[110],"We":[112,132,156],"tensorize":[113],"output":[115],"space":[116],"introduce":[118],"tensor/matrix-normal":[119],"posteriors":[121],"to":[122,128,152],"capture":[123],"correlations":[126],"reduce":[129],"parameters.":[131],"jointly":[133],"optimize":[134],"exploit":[139],"inherent":[141],"Kronecker":[142],"product":[143],"in":[145,163],"model":[148],"evidence":[149],"lower":[150],"bound":[151],"accelerate":[153],"computation.":[155],"demonstrate":[157],"advantages":[159],"our":[161],"method":[162],"several":[164],"real-world":[165],"applications.":[166]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2021,"cited_by_count":2}],"updated_date":"2026-08-06T08:24:18.245995","created_date":"2025-10-10T00:00:00"}