{"id":"https://openalex.org/W4394585964","doi":"https://doi.org/10.1109/tnnls.2022.3200602","title":"Asynchronous Parallel Large-Scale Gaussian Process Regression","display_name":"Asynchronous Parallel Large-Scale Gaussian Process Regression","publication_year":2024,"publication_date":"2024-04-08","ids":{"openalex":"https://openalex.org/W4394585964","doi":"https://doi.org/10.1109/tnnls.2022.3200602","pmid":"https://pubmed.ncbi.nlm.nih.gov/38587955"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2022.3200602","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2022.3200602","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["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 Neural Networks and Learning Systems","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/A5055404586","display_name":"Zhiyuan Dang","orcid":"https://orcid.org/0000-0003-4241-4116"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhiyuan Dang","raw_affiliation_strings":["School of Electronic Engineering, Xidian University, Xi&#x2019;an, China"],"affiliations":[{"raw_affiliation_string":"School of Electronic Engineering, Xidian University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069728539","display_name":"Bin Gu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210113480","display_name":"Mohamed bin Zayed University of Artificial Intelligence","ror":"https://ror.org/0258gkt32","country_code":"AE","type":"education","lineage":["https://openalex.org/I4210113480"]}],"countries":["AE"],"is_corresponding":false,"raw_author_name":"Bin Gu","raw_affiliation_strings":["Department of Machine Learning, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates"],"affiliations":[{"raw_affiliation_string":"Department of Machine Learning, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates","institution_ids":["https://openalex.org/I4210113480"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015874725","display_name":"Cheng Deng","orcid":"https://orcid.org/0000-0003-2620-3247"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cheng Deng","raw_affiliation_strings":["School of Electronic Engineering, Xidian University, Xi&#x2019;an, China"],"affiliations":[{"raw_affiliation_string":"School of Electronic Engineering, Xidian University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060016795","display_name":"Heng Huang","orcid":"https://orcid.org/0000-0002-3483-8333"},"institutions":[{"id":"https://openalex.org/I170201317","display_name":"University of Pittsburgh","ror":"https://ror.org/01an3r305","country_code":"US","type":"education","lineage":["https://openalex.org/I170201317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Heng Huang","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I170201317"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5055404586"],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":null,"apc_paid":null,"fwci":1.4548,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.83607884,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"35","issue":"6","first_page":"8683","last_page":"8694"},"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":0.993399977684021,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9894999861717224,"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":0.6045350432395935},{"id":"https://openalex.org/keywords/stochastic-gradient-descent","display_name":"Stochastic gradient descent","score":0.5991853475570679},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5934820175170898},{"id":"https://openalex.org/keywords/kriging","display_name":"Kriging","score":0.590735137462616},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.4808802902698517},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.4773668944835663},{"id":"https://openalex.org/keywords/ground-penetrating-radar","display_name":"Ground-penetrating radar","score":0.4754745066165924},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.42430388927459717},{"id":"https://openalex.org/keywords/coordinate-descent","display_name":"Coordinate descent","score":0.42252790927886963},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.4041132926940918},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3933524787425995},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3865160346031189},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.3661978840827942},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3577083945274353},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.28059595823287964},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.25929394364356995},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.13937783241271973},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.12075778841972351}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6045350432395935},{"id":"https://openalex.org/C206688291","wikidata":"https://www.wikidata.org/wiki/Q7617819","display_name":"Stochastic gradient descent","level":3,"score":0.5991853475570679},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5934820175170898},{"id":"https://openalex.org/C81692654","wikidata":"https://www.wikidata.org/wiki/Q225926","display_name":"Kriging","level":2,"score":0.590735137462616},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.4808802902698517},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.4773668944835663},{"id":"https://openalex.org/C71813955","wikidata":"https://www.wikidata.org/wiki/Q503560","display_name":"Ground-penetrating radar","level":3,"score":0.4754745066165924},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.42430388927459717},{"id":"https://openalex.org/C157553263","wikidata":"https://www.wikidata.org/wiki/Q5168004","display_name":"Coordinate descent","level":2,"score":0.42252790927886963},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4041132926940918},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3933524787425995},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3865160346031189},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.3661978840827942},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3577083945274353},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.28059595823287964},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.25929394364356995},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.13937783241271973},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.12075778841972351},{"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/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","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/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/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tnnls.2022.3200602","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2022.3200602","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["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 Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:38587955","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/38587955","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 neural networks and learning systems","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1372603746","display_name":null,"funder_award_id":"62171343","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G1816562919","display_name":null,"funder_award_id":"2021ZDLGY01-03","funder_id":"https://openalex.org/F4320336350","funder_display_name":"Key Research and Development Projects of Shaanxi Province"},{"id":"https://openalex.org/G3509175749","display_name":null,"funder_award_id":"ZDRC2102","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G5089867355","display_name":null,"funder_award_id":"62071361","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G986958842","display_name":null,"funder_award_id":"62132016","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null},{"id":"https://openalex.org/F4320336350","display_name":"Key Research and Development Projects of Shaanxi Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":52,"referenced_works":["https://openalex.org/W241901432","https://openalex.org/W995630646","https://openalex.org/W1540155273","https://openalex.org/W1556859836","https://openalex.org/W1577493656","https://openalex.org/W1743803520","https://openalex.org/W1777124189","https://openalex.org/W1977581467","https://openalex.org/W2014158063","https://openalex.org/W2014903366","https://openalex.org/W2027248184","https://openalex.org/W2039050532","https://openalex.org/W2079482358","https://openalex.org/W2091825929","https://openalex.org/W2146897752","https://openalex.org/W2150621701","https://openalex.org/W2170912685","https://openalex.org/W2250193512","https://openalex.org/W2952594493","https://openalex.org/W2962952793","https://openalex.org/W2963479359","https://openalex.org/W2963542673","https://openalex.org/W2997060212","https://openalex.org/W3000508506","https://openalex.org/W3080261024","https://openalex.org/W3082981620","https://openalex.org/W3087656255","https://openalex.org/W3182947351","https://openalex.org/W3183420805","https://openalex.org/W3184680419","https://openalex.org/W4211049957","https://openalex.org/W4250589301","https://openalex.org/W6609413351","https://openalex.org/W6630841318","https://openalex.org/W6637703051","https://openalex.org/W6637968757","https://openalex.org/W6639724578","https://openalex.org/W6640000427","https://openalex.org/W6675069819","https://openalex.org/W6680853513","https://openalex.org/W6681302627","https://openalex.org/W6681439488","https://openalex.org/W6681460771","https://openalex.org/W6682143360","https://openalex.org/W6684109119","https://openalex.org/W6720558605","https://openalex.org/W6730070846","https://openalex.org/W6738980247","https://openalex.org/W6764647576","https://openalex.org/W6766284779","https://openalex.org/W6776213335","https://openalex.org/W6787382039"],"related_works":["https://openalex.org/W4315471419","https://openalex.org/W2946057701","https://openalex.org/W4286693783","https://openalex.org/W3175914740","https://openalex.org/W4294982320","https://openalex.org/W3034587794","https://openalex.org/W3210805454","https://openalex.org/W2020098476","https://openalex.org/W2356529274","https://openalex.org/W2953288298"],"abstract_inverted_index":{"Gaussian":[0],"process":[1],"regression":[2,167],"(GPR)":[3],"is":[4,19,26],"an":[5,51],"important":[6],"nonparametric":[7],"learning":[8,12],"method":[9,95],"in":[10,46,79,123,132],"machine":[11],"research":[13],"with":[14,165],"many":[15],"real-world":[16],"applications.":[17],"It":[18],"well":[20,131],"known":[21],"that":[22,148,172],"training":[23,61,142],"large-scale":[24,60,162],"GPR":[25,67,180],"a":[27,69,152],"challenging":[28,44],"task":[29],"due":[30],"to":[31,57,68,81,96,117],"the":[32,59,66,91,98,119,141,173,177],"required":[33],"heavy":[34],"computational":[35],"cost":[36],"and":[37,101,113,122,136,138,168],"large":[38],"volume":[39],"memory.":[40],"To":[41],"address":[42],"this":[43,47,84,126],"problem,":[45,72,87],"article,":[48],"we":[49,88,146],"propose":[50],"asynchronous":[52],"doubly":[53],"stochastic":[54,106,109,114],"gradient":[55,112],"algorithm":[56,129,150,175],"handle":[58],"of":[62],"GPR.":[63],"We":[64],"formulate":[65],"convex":[70,85],"optimization":[71],"i.e.,":[73,108],"kernel":[74,86,99],"ridge":[75],"regression.":[76],"After":[77],"that,":[78],"order":[80],"efficiently":[82],"solve":[83],"first":[89],"use":[90],"random":[92],"feature":[93],"mapping":[94],"approximate":[97],"model":[100],"then":[102],"utilize":[103],"two":[104],"unbiased":[105],"approximations,":[107],"variance":[110],"reduced":[111],"coordinate":[115],"descent,":[116],"update":[118],"solution":[120],"asynchronously":[121],"parallel.":[124],"In":[125],"way,":[127],"our":[128,149],"scales":[130],"both":[133,166],"sample":[134],"size":[135],"dimensionality,":[137],"speeds":[139],"up":[140],"computation.":[143],"More":[144],"importantly,":[145],"prove":[147],"has":[151],"global":[153],"linear":[154],"convergence":[155],"rate.":[156],"Our":[157],"experimental":[158],"results":[159],"on":[160],"eight":[161],"benchmark":[163],"datasets":[164],"classification":[169],"tasks":[170],"show":[171],"proposed":[174],"outperforms":[176],"existing":[178],"state-of-the-art":[179],"methods.":[181]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
