{"id":"https://openalex.org/W3093941497","doi":"https://doi.org/10.1109/mlsp49062.2020.9231601","title":"Approximate Gaussian Process Regression and Performance Analysis Using Composite Likelihood","display_name":"Approximate Gaussian Process Regression and Performance Analysis Using Composite Likelihood","publication_year":2020,"publication_date":"2020-09-01","ids":{"openalex":"https://openalex.org/W3093941497","doi":"https://doi.org/10.1109/mlsp49062.2020.9231601","mag":"3093941497"},"language":"en","primary_location":{"id":"doi:10.1109/mlsp49062.2020.9231601","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp49062.2020.9231601","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-article"},"type":"article","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/A5100726013","display_name":"Xiuming Liu","orcid":"https://orcid.org/0000-0001-6783-9351"},"institutions":[{"id":"https://openalex.org/I123387679","display_name":"Uppsala University","ror":"https://ror.org/048a87296","country_code":"SE","type":"education","lineage":["https://openalex.org/I123387679"]}],"countries":["SE"],"is_corresponding":true,"raw_author_name":"Xiuming Liu","raw_affiliation_strings":["Uppsala University, Uppsala, Sweden"],"affiliations":[{"raw_affiliation_string":"Uppsala University, Uppsala, Sweden","institution_ids":["https://openalex.org/I123387679"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043094282","display_name":"Dave Zachariah","orcid":"https://orcid.org/0000-0002-6698-0166"},"institutions":[{"id":"https://openalex.org/I123387679","display_name":"Uppsala University","ror":"https://ror.org/048a87296","country_code":"SE","type":"education","lineage":["https://openalex.org/I123387679"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Dave Zachariah","raw_affiliation_strings":["Uppsala University, Uppsala, Sweden"],"affiliations":[{"raw_affiliation_string":"Uppsala University, Uppsala, Sweden","institution_ids":["https://openalex.org/I123387679"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5077317339","display_name":"Edith C.\u2010H. Ngai","orcid":"https://orcid.org/0000-0002-3454-8731"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Edith C.H. Ngai","raw_affiliation_strings":["The Chinese University of Hong Kong, Hong Kong, China"],"affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I177725633"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100726013"],"corresponding_institution_ids":["https://openalex.org/I123387679"],"apc_list":null,"apc_paid":null,"fwci":0.1326,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.554315,"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":"1","last_page":"6"},"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/T11236","display_name":"Control Systems and Identification","score":0.9829999804496765,"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"}},{"id":"https://openalex.org/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.9521999955177307,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.7579658627510071},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6267497539520264},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5778810977935791},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.568027138710022},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5083088278770447},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4821057617664337},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46575409173965454},{"id":"https://openalex.org/keywords/kriging","display_name":"Kriging","score":0.46426913142204285},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4610729515552521},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.43537867069244385},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.4307589828968048},{"id":"https://openalex.org/keywords/nonparametric-statistics","display_name":"Nonparametric statistics","score":0.4300433397293091},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.41962963342666626},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.38190189003944397},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.37110501527786255},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3143271207809448},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.17654013633728027}],"concepts":[{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.7579658627510071},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6267497539520264},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5778810977935791},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.568027138710022},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5083088278770447},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4821057617664337},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46575409173965454},{"id":"https://openalex.org/C81692654","wikidata":"https://www.wikidata.org/wiki/Q225926","display_name":"Kriging","level":2,"score":0.46426913142204285},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4610729515552521},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.43537867069244385},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.4307589828968048},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.4300433397293091},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.41962963342666626},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.38190189003944397},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.37110501527786255},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3143271207809448},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.17654013633728027},{"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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mlsp49062.2020.9231601","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp49062.2020.9231601","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP)","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":22,"referenced_works":["https://openalex.org/W69468016","https://openalex.org/W88520345","https://openalex.org/W137285897","https://openalex.org/W1514558801","https://openalex.org/W1525535255","https://openalex.org/W1571870753","https://openalex.org/W1746819321","https://openalex.org/W1899852503","https://openalex.org/W1923274812","https://openalex.org/W1973310094","https://openalex.org/W2099111195","https://openalex.org/W2112545207","https://openalex.org/W2136634080","https://openalex.org/W2243651912","https://openalex.org/W2414498849","https://openalex.org/W3099367651","https://openalex.org/W3099663651","https://openalex.org/W3139866291","https://openalex.org/W4211049957","https://openalex.org/W4232464081","https://openalex.org/W6605566567","https://openalex.org/W6640330755"],"related_works":["https://openalex.org/W566010457","https://openalex.org/W2600092203","https://openalex.org/W4293503520","https://openalex.org/W4300066510","https://openalex.org/W2056958800","https://openalex.org/W2803685231","https://openalex.org/W3134152097","https://openalex.org/W4311388919","https://openalex.org/W2966696655","https://openalex.org/W1964286703"],"abstract_inverted_index":{"Nonparametric":[0],"regression":[1],"using":[2,60],"Gaussian":[3],"Process":[4],"(GP)":[5],"models":[6],"is":[7],"a":[8,46],"powerful":[9],"but":[10],"computationally":[11,64],"demanding":[12],"method.":[13],"While":[14],"various":[15,54],"approximation":[16,80],"methods":[17],"have":[18,28],"been":[19],"developed":[20],"to":[21],"mitigate":[22],"its":[23],"computation":[24],"complexity,":[25],"few":[26],"works":[27],"addressed":[29],"the":[30,33,37,76,79],"quality":[31,77],"of":[32,36,78,83],"resulting":[34],"approximations":[35,66],"target":[38],"posterior.":[39],"In":[40],"this":[41],"paper":[42],"we":[43],"start":[44],"from":[45],"general":[47],"belief":[48],"updating":[49],"framework":[50],"that":[51,58],"can":[52],"generate":[53],"approximations.":[55],"We":[56,73],"show":[57],"applying":[59],"composite":[61],"likelihoods":[62],"yields":[63],"scalable":[65],"for":[67],"both":[68],"GP":[69],"learning":[70],"and":[71],"prediction.":[72],"then":[74],"analyze":[75],"in":[81],"terms":[82],"averaged":[84],"prediction":[85],"errors":[86],"as":[87,89],"well":[88],"Kullback-Leibler":[90],"(KL)":[91],"divergences.":[92]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}