{"id":"https://openalex.org/W2939022137","doi":"https://doi.org/10.1109/icassp.2019.8682350","title":"Scalable Gaussian Process Using Inexact Admm for Big Data","display_name":"Scalable Gaussian Process Using Inexact Admm for Big Data","publication_year":2019,"publication_date":"2019-04-17","ids":{"openalex":"https://openalex.org/W2939022137","doi":"https://doi.org/10.1109/icassp.2019.8682350","mag":"2939022137"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2019.8682350","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2019.8682350","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5080546910","display_name":"Yue Xu","orcid":"https://orcid.org/0000-0003-4074-4357"},"institutions":[{"id":"https://openalex.org/I4210099586","display_name":"Shenzhen Research Institute of Big Data","ror":"https://ror.org/00z1gwf89","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210099586"]},{"id":"https://openalex.org/I4210116924","display_name":"Chinese University of Hong Kong, Shenzhen","ror":"https://ror.org/02d5ks197","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633","https://openalex.org/I180726961","https://openalex.org/I4210116924"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yue Xu","raw_affiliation_strings":["The Chinese University of Hong Kong, Shenzhen and SRIBD"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong, Shenzhen and SRIBD","institution_ids":["https://openalex.org/I4210099586","https://openalex.org/I4210116924"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100696174","display_name":"Feng Yin","orcid":"https://orcid.org/0000-0001-5754-9246"},"institutions":[{"id":"https://openalex.org/I4210099586","display_name":"Shenzhen Research Institute of Big Data","ror":"https://ror.org/00z1gwf89","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210099586"]},{"id":"https://openalex.org/I4210116924","display_name":"Chinese University of Hong Kong, Shenzhen","ror":"https://ror.org/02d5ks197","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633","https://openalex.org/I180726961","https://openalex.org/I4210116924"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feng Yin","raw_affiliation_strings":["The Chinese University of Hong Kong, Shenzhen and SRIBD"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong, Shenzhen and SRIBD","institution_ids":["https://openalex.org/I4210099586","https://openalex.org/I4210116924"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100462845","display_name":"Jiawei Zhang","orcid":"https://orcid.org/0000-0002-8634-1687"},"institutions":[{"id":"https://openalex.org/I4210099586","display_name":"Shenzhen Research Institute of Big Data","ror":"https://ror.org/00z1gwf89","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210099586"]},{"id":"https://openalex.org/I4210116924","display_name":"Chinese University of Hong Kong, Shenzhen","ror":"https://ror.org/02d5ks197","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633","https://openalex.org/I180726961","https://openalex.org/I4210116924"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiawei Zhang","raw_affiliation_strings":["The Chinese University of Hong Kong, Shenzhen and SRIBD"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong, Shenzhen and SRIBD","institution_ids":["https://openalex.org/I4210099586","https://openalex.org/I4210116924"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059494893","display_name":"Wenjun Xu","orcid":"https://orcid.org/0000-0001-8767-4742"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenjun Xu","raw_affiliation_strings":["Key Lab of Universal Wireless Communications, Beijing University of Posts and Telecommunications"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Lab of Universal Wireless Communications, Beijing University of Posts and Telecommunications","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009164482","display_name":"Shuguang Cui","orcid":"https://orcid.org/0000-0003-2608-775X"},"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":"Shuguang Cui","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of California, Davis"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of California, Davis","institution_ids":["https://openalex.org/I84218800"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101766226","display_name":"Zhi\u2010Quan Luo","orcid":"https://orcid.org/0000-0003-3995-914X"},"institutions":[{"id":"https://openalex.org/I4210099586","display_name":"Shenzhen Research Institute of Big Data","ror":"https://ror.org/00z1gwf89","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210099586"]},{"id":"https://openalex.org/I4210116924","display_name":"Chinese University of Hong Kong, Shenzhen","ror":"https://ror.org/02d5ks197","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633","https://openalex.org/I180726961","https://openalex.org/I4210116924"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhi-Quan Luo","raw_affiliation_strings":["The Chinese University of Hong Kong, Shenzhen and SRIBD"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong, Shenzhen and SRIBD","institution_ids":["https://openalex.org/I4210099586","https://openalex.org/I4210116924"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2187,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.49266205,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"6","issue":null,"first_page":"7495","last_page":"7499"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":1.0,"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":1.0,"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":0.9925000071525574,"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/T11236","display_name":"Control Systems and Identification","score":0.9886999726295471,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/parallelizable-manifold","display_name":"Parallelizable manifold","score":0.9252594709396362},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.8429762125015259},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8191509246826172},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.683585524559021},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.6621755361557007},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.6023330092430115},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5099712610244751},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.42253604531288147},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.4137345850467682},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3713269829750061},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3427790403366089},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3247618079185486},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2778494358062744},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.24631163477897644},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10358050465583801}],"concepts":[{"id":"https://openalex.org/C148047603","wikidata":"https://www.wikidata.org/wiki/Q1014612","display_name":"Parallelizable manifold","level":2,"score":0.9252594709396362},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.8429762125015259},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8191509246826172},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.683585524559021},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.6621755361557007},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.6023330092430115},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5099712610244751},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.42253604531288147},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.4137345850467682},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3713269829750061},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3427790403366089},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3247618079185486},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2778494358062744},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.24631163477897644},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10358050465583801},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.2019.8682350","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2019.8682350","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W137285897","https://openalex.org/W1571870753","https://openalex.org/W1911299338","https://openalex.org/W1973310094","https://openalex.org/W1999479532","https://openalex.org/W2097795807","https://openalex.org/W2112545207","https://openalex.org/W2131241448","https://openalex.org/W2164278908","https://openalex.org/W2295652899","https://openalex.org/W2564963669","https://openalex.org/W2783656730","https://openalex.org/W2890032783","https://openalex.org/W2963754333","https://openalex.org/W4211049957","https://openalex.org/W4292363360","https://openalex.org/W4312258136","https://openalex.org/W6605566567","https://openalex.org/W6640000427","https://openalex.org/W6678911119"],"related_works":["https://openalex.org/W4285069850","https://openalex.org/W1974634278","https://openalex.org/W4240963716","https://openalex.org/W2891970004","https://openalex.org/W4301075503","https://openalex.org/W1596637634","https://openalex.org/W1996916724","https://openalex.org/W2321918740","https://openalex.org/W1964286703","https://openalex.org/W2169866437"],"abstract_inverted_index":{"Gaussian":[0],"process":[1],"(GP)":[2],"for":[3,47],"machine":[4],"learning":[5],"has":[6],"been":[7],"well":[8],"studied":[9],"over":[10,91,109],"the":[11,24,62,67,101,104,110],"past":[12],"two":[13],"decades":[14],"and":[15],"is":[16,89],"now":[17],"widely":[18],"used":[19],"in":[20],"many":[21],"sectors.":[22],"However,":[23],"design":[25],"of":[26,55,83,95,103],"low-complexity":[27],"GP":[28,44,107],"models":[29],"still":[30],"remains":[31],"a":[32,41,52,76,92],"challenging":[33],"research":[34],"problem.":[35],"In":[36,59],"this":[37],"paper,":[38],"we":[39,65],"propose":[40],"novel":[42],"scalable":[43,106],"regression":[45],"model":[46,108],"processing":[48],"big":[49],"datasets,":[50],"using":[51],"large":[53,93],"number":[54,94],"parallel":[56],"computation":[57,96],"units.":[58,97],"contrast":[60],"to":[61],"existing":[63],"methods,":[64],"solve":[66],"classic":[68],"maximum":[69],"likelihood":[70],"based":[71],"hyper-parameter":[72],"optimization":[73],"problem":[74],"by":[75],"carefully":[77],"designed":[78],"distributed":[79,112],"alternating":[80],"direction":[81],"method":[82,88],"multipliers":[84],"(ADMM).":[85],"The":[86],"proposed":[87,105],"parallelizable":[90],"Simulation":[98],"results":[99],"confirm":[100],"benefits":[102],"state-of-the-art":[111],"methods.":[113]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}