{"id":"https://openalex.org/W3204974012","doi":"https://doi.org/10.1109/tpami.2022.3157197","title":"Incremental Ensemble Gaussian Processes","display_name":"Incremental Ensemble Gaussian Processes","publication_year":2022,"publication_date":"2022-03-07","ids":{"openalex":"https://openalex.org/W3204974012","doi":"https://doi.org/10.1109/tpami.2022.3157197","mag":"3204974012","pmid":"https://pubmed.ncbi.nlm.nih.gov/35254977"},"language":"en","primary_location":{"id":"doi:10.1109/tpami.2022.3157197","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2022.3157197","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/A5101509596","display_name":"Qin Lu","orcid":"https://orcid.org/0000-0002-4051-1396"},"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":true,"raw_author_name":"Qin Lu","raw_affiliation_strings":["Department of Electrical and Computer Engineering and Digital Technology Center, University of Minnesota, Minneapolis, MN, USA"],"raw_orcid":"https://orcid.org/0000-0002-4051-1396","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering and Digital Technology Center, University of Minnesota, Minneapolis, MN, USA","institution_ids":["https://openalex.org/I130238516"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066954132","display_name":"Georgios V. Karanikolas","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":"Georgios V. Karanikolas","raw_affiliation_strings":["Department of Electrical and Computer Engineering and Digital Technology Center, University of Minnesota, Minneapolis, MN, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering and Digital Technology Center, University of Minnesota, Minneapolis, MN, USA","institution_ids":["https://openalex.org/I130238516"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026758314","display_name":"Georgios B. Giannakis","orcid":"https://orcid.org/0000-0002-0196-0260"},"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":"Georgios B. Giannakis","raw_affiliation_strings":["Department of Electrical and Computer Engineering and Digital Technology Center, University of Minnesota, Minneapolis, MN, USA"],"raw_orcid":"https://orcid.org/0000-0002-0196-0260","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering and Digital Technology Center, University of Minnesota, Minneapolis, MN, USA","institution_ids":["https://openalex.org/I130238516"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101509596"],"corresponding_institution_ids":["https://openalex.org/I130238516"],"apc_list":null,"apc_paid":null,"fwci":2.7775,"has_fulltext":false,"cited_by_count":27,"citation_normalized_percentile":{"value":0.91402076,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"45","issue":"2","first_page":"1876","last_page":"1893"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9828000068664551,"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9790999889373779,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.729750394821167},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6211109757423401},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.6134828329086304},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5923861265182495},{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.5920915603637695},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5010988712310791},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.47559890151023865},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.4381265640258789},{"id":"https://openalex.org/keywords/multiple-kernel-learning","display_name":"Multiple kernel learning","score":0.4356970489025116},{"id":"https://openalex.org/keywords/bayesian-optimization","display_name":"Bayesian optimization","score":0.42967379093170166},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4234212636947632},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.42267265915870667},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.41699302196502686},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.3472476303577423},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.28808045387268066},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.21707239747047424},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1436983346939087}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.729750394821167},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6211109757423401},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.6134828329086304},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5923861265182495},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.5920915603637695},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5010988712310791},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.47559890151023865},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.4381265640258789},{"id":"https://openalex.org/C2776879701","wikidata":"https://www.wikidata.org/wiki/Q25048660","display_name":"Multiple kernel learning","level":4,"score":0.4356970489025116},{"id":"https://openalex.org/C2778049539","wikidata":"https://www.wikidata.org/wiki/Q17002908","display_name":"Bayesian optimization","level":2,"score":0.42967379093170166},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4234212636947632},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.42267265915870667},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.41699302196502686},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.3472476303577423},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.28808045387268066},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.21707239747047424},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1436983346939087},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","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/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tpami.2022.3157197","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2022.3157197","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:35254977","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/35254977","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":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.5799999833106995}],"awards":[{"id":"https://openalex.org/G2521329623","display_name":null,"funder_award_id":"1901134","funder_id":"https://openalex.org/F4320335353","funder_display_name":"National Science Foundation of Sri Lanka"},{"id":"https://openalex.org/G4990106386","display_name":null,"funder_award_id":"2126052","funder_id":"https://openalex.org/F4320335353","funder_display_name":"National Science Foundation of Sri Lanka"},{"id":"https://openalex.org/G7979682668","display_name":null,"funder_award_id":"2128593","funder_id":"https://openalex.org/F4320335353","funder_display_name":"National Science Foundation of Sri Lanka"}],"funders":[{"id":"https://openalex.org/F4320335353","display_name":"National Science Foundation of Sri Lanka","ror":"https://ror.org/010xaa060"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":109,"referenced_works":["https://openalex.org/W137285897","https://openalex.org/W1503398984","https://openalex.org/W1505866674","https://openalex.org/W1537838346","https://openalex.org/W1570963478","https://openalex.org/W1571870753","https://openalex.org/W1746819321","https://openalex.org/W1871194732","https://openalex.org/W1886178467","https://openalex.org/W1897171481","https://openalex.org/W1911299338","https://openalex.org/W1973310094","https://openalex.org/W2012725996","https://openalex.org/W2014903366","https://openalex.org/W2020166044","https://openalex.org/W2040731319","https://openalex.org/W2054799446","https://openalex.org/W2080006911","https://openalex.org/W2083780116","https://openalex.org/W2096772472","https://openalex.org/W2098949458","https://openalex.org/W2099768828","https://openalex.org/W2100659887","https://openalex.org/W2107386393","https://openalex.org/W2110404686","https://openalex.org/W2111683289","https://openalex.org/W2117063635","https://openalex.org/W2121033924","https://openalex.org/W2129564505","https://openalex.org/W2133140216","https://openalex.org/W2136111243","https://openalex.org/W2139047213","https://openalex.org/W2139320579","https://openalex.org/W2140095548","https://openalex.org/W2142959074","https://openalex.org/W2144902422","https://openalex.org/W2146610201","https://openalex.org/W2151242073","https://openalex.org/W2158822024","https://openalex.org/W2170912685","https://openalex.org/W2170952786","https://openalex.org/W2172114485","https://openalex.org/W2278171434","https://openalex.org/W2287294391","https://openalex.org/W2294767448","https://openalex.org/W2408432900","https://openalex.org/W2513180554","https://openalex.org/W2559331598","https://openalex.org/W2615761526","https://openalex.org/W2617168490","https://openalex.org/W2620689287","https://openalex.org/W2624473996","https://openalex.org/W2893995718","https://openalex.org/W2953071055","https://openalex.org/W2953234161","https://openalex.org/W2963034269","https://openalex.org/W2963469388","https://openalex.org/W2963976431","https://openalex.org/W2964206586","https://openalex.org/W2964385158","https://openalex.org/W2966324907","https://openalex.org/W2971353969","https://openalex.org/W2980178929","https://openalex.org/W3037711500","https://openalex.org/W3037849999","https://openalex.org/W3112073073","https://openalex.org/W3120740533","https://openalex.org/W3159584340","https://openalex.org/W4205841652","https://openalex.org/W4206212643","https://openalex.org/W4211049957","https://openalex.org/W4288413034","https://openalex.org/W4293877676","https://openalex.org/W6605566567","https://openalex.org/W6629917196","https://openalex.org/W6639253883","https://openalex.org/W6639524736","https://openalex.org/W6639724578","https://openalex.org/W6640000427","https://openalex.org/W6674439305","https://openalex.org/W6674660115","https://openalex.org/W6674989108","https://openalex.org/W6675823452","https://openalex.org/W6676272486","https://openalex.org/W6676528262","https://openalex.org/W6678059867","https://openalex.org/W6680375596","https://openalex.org/W6681302627","https://openalex.org/W6681609451","https://openalex.org/W6682250648","https://openalex.org/W6683138052","https://openalex.org/W6685474145","https://openalex.org/W6693534576","https://openalex.org/W6695061060","https://openalex.org/W6696104039","https://openalex.org/W6697202336","https://openalex.org/W6714150371","https://openalex.org/W6730022233","https://openalex.org/W6730064250","https://openalex.org/W6738196418","https://openalex.org/W6738403115","https://openalex.org/W6739180466","https://openalex.org/W6748213982","https://openalex.org/W6755463424","https://openalex.org/W6760385676","https://openalex.org/W6768732345","https://openalex.org/W6779892638","https://openalex.org/W6787699512","https://openalex.org/W6791487080"],"related_works":["https://openalex.org/W3104422856","https://openalex.org/W4206864338","https://openalex.org/W4287867179","https://openalex.org/W3134690064","https://openalex.org/W4210726438","https://openalex.org/W4287752080","https://openalex.org/W3007674363","https://openalex.org/W3118984993","https://openalex.org/W2860329578","https://openalex.org/W3037706579"],"abstract_inverted_index":{"Belonging":[0],"to":[1,89,103,119,130],"the":[2,30,63,99,112,121,125,139,149,158,162,174,179,188,199,209,212],"family":[3],"of":[4,23,80,151,176,211],"Bayesian":[5],"nonparametrics,":[6],"Gaussian":[7],"process":[8],"(GP)":[9],"based":[10],"approaches":[11],"have":[12],"well-documented":[13],"merits":[14],"not":[15,187],"only":[16],"in":[17,28,48,55,157,181],"learning":[18,192],"over":[19],"a":[20,39,85,90],"rich":[21],"class":[22],"nonlinear":[24],"functions,":[25],"but":[26,186],"also":[27],"quantifying":[29],"associated":[31],"uncertainty.":[32],"However,":[33],"most":[34],"GP":[35,71,81,96,145],"methods":[36],"rely":[37],"on":[38,116],"single":[40],"preselected":[41],"kernel":[42,61,87,92],"function,":[43],"which":[44],"may":[45],"fall":[46],"short":[47],"characterizing":[49],"data":[50,206],"samples":[51],"that":[52],"arrive":[53],"sequentially":[54],"time-critical":[56],"applications.":[57],"To":[58,147],"enable":[59],"online":[60,105,182,190],"adaptation,":[62],"present":[64],"work":[65],"advocates":[66],"an":[67,74,78],"incremental":[68],"ensemble":[69,79],"(IE-)":[70],"framework,":[72],"where":[73,161],"EGP":[75,113,140],"assembler":[76,114,141],"employs":[77],"learners,":[82],"each":[83,95,144],"having":[84],"unique":[86],"belonging":[88],"prescribed":[91],"dictionary.":[93],"With":[94],"expert":[97],"leveraging":[98],"random":[100],"feature-based":[101],"approximation":[102],"perform":[104],"prediction":[106],"and":[107,142,153,204],"model":[108],"update":[109],"with":[110],"scalability,":[111],"capitalizes":[115],"data-adaptive":[117],"weights":[118],"synthesize":[120],"per-expert":[122],"predictions.":[123],"Further,":[124],"novel":[126,200],"IE-GP":[127,152,201],"is":[128,196],"generalized":[129],"accommodate":[131],"time-varying":[132],"functions":[133],"by":[134],"modeling":[135,163],"structured":[136],"dynamics":[137],"at":[138],"within":[143],"learner.":[146],"benchmark":[148],"performance":[150,168],"its":[154],"dynamic":[155],"variant":[156],"adversarial":[159],"setting":[160],"assumptions":[164],"are":[165],"violated,":[166],"rigorous":[167],"analysis":[169],"has":[170],"been":[171],"conducted":[172],"via":[173],"notion":[175],"regret,":[177],"as":[178],"norm":[180],"convex":[183],"optimization.":[184],"Last":[185],"least,":[189],"unsupervised":[191],"for":[193],"dimensionality":[194],"reduction":[195],"explored":[197],"under":[198],"framework.":[202],"Synthetic":[203],"real":[205],"tests":[207],"demonstrate":[208],"effectiveness":[210],"proposed":[213],"schemes.":[214]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":5}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}