{"id":"https://openalex.org/W4390661296","doi":"https://doi.org/10.1007/s10994-023-06491-x","title":"Fast deep mixtures of Gaussian process experts","display_name":"Fast deep mixtures of Gaussian process experts","publication_year":2024,"publication_date":"2024-01-08","ids":{"openalex":"https://openalex.org/W4390661296","doi":"https://doi.org/10.1007/s10994-023-06491-x"},"language":"en","primary_location":{"id":"doi:10.1007/s10994-023-06491-x","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-023-06491-x","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-023-06491-x.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s10994-023-06491-x.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5081430775","display_name":"Clement Etienam","orcid":null},"institutions":[{"id":"https://openalex.org/I1304085615","display_name":"Nvidia (United Kingdom)","ror":"https://ror.org/02kr42612","country_code":"GB","type":"company","lineage":["https://openalex.org/I1304085615","https://openalex.org/I4210127875"]},{"id":"https://openalex.org/I28407311","display_name":"University of Manchester","ror":"https://ror.org/027m9bs27","country_code":"GB","type":"education","lineage":["https://openalex.org/I28407311"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Clement Etienam","raw_affiliation_strings":["Department of Mathematics, University of Manchester, Manchester, UK","NVIDIA, Reading, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mathematics, University of Manchester, Manchester, UK","institution_ids":["https://openalex.org/I28407311"]},{"raw_affiliation_string":"NVIDIA, Reading, UK","institution_ids":["https://openalex.org/I1304085615"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053058869","display_name":"Kody J. H. Law","orcid":"https://orcid.org/0000-0003-3133-2537"},"institutions":[{"id":"https://openalex.org/I28407311","display_name":"University of Manchester","ror":"https://ror.org/027m9bs27","country_code":"GB","type":"education","lineage":["https://openalex.org/I28407311"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Kody J. H. Law","raw_affiliation_strings":["Department of Mathematics, University of Manchester, Manchester, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mathematics, University of Manchester, Manchester, UK","institution_ids":["https://openalex.org/I28407311"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067971785","display_name":"Sara Wade","orcid":"https://orcid.org/0000-0002-6547-5555"},"institutions":[{"id":"https://openalex.org/I98677209","display_name":"University of Edinburgh","ror":"https://ror.org/01nrxwf90","country_code":"GB","type":"education","lineage":["https://openalex.org/I98677209"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Sara Wade","raw_affiliation_strings":["School of Mathematics, University of Edinburgh, Edinburgh, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mathematics, University of Edinburgh, Edinburgh, UK","institution_ids":["https://openalex.org/I98677209"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063002544","display_name":"Vitaly Zankin","orcid":"https://orcid.org/0000-0003-0674-3721"},"institutions":[{"id":"https://openalex.org/I28407311","display_name":"University of Manchester","ror":"https://ror.org/027m9bs27","country_code":"GB","type":"education","lineage":["https://openalex.org/I28407311"]},{"id":"https://openalex.org/I4210128584","display_name":"The Alan Turing Institute","ror":"https://ror.org/035dkdb55","country_code":"GB","type":"facility","lineage":["https://openalex.org/I4210128584"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Vitaly Zankin","raw_affiliation_strings":["Department of Mathematics, University of Manchester, Manchester, UK","The Alan Turing Institute, London, UK"],"raw_orcid":"https://orcid.org/0000-0003-0674-3721","affiliations":[{"raw_affiliation_string":"Department of Mathematics, University of Manchester, Manchester, UK","institution_ids":["https://openalex.org/I28407311"]},{"raw_affiliation_string":"The Alan Turing Institute, London, UK","institution_ids":["https://openalex.org/I4210128584"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5063002544"],"corresponding_institution_ids":["https://openalex.org/I28407311","https://openalex.org/I4210128584"],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":1.6146,"has_fulltext":true,"cited_by_count":5,"citation_normalized_percentile":{"value":0.84767796,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"113","issue":"3","first_page":"1483","last_page":"1508"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9950000047683716,"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":0.9801999926567078,"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/computer-science","display_name":"Computer science","score":0.6947609186172485},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.629751443862915},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.5546379685401917},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5425101518630981},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.541970431804657},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5025451183319092},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.49855899810791016},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.49414733052253723},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.49383577704429626},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.44937658309936523},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4458332359790802},{"id":"https://openalex.org/keywords/maximum-a-posteriori-estimation","display_name":"Maximum a posteriori estimation","score":0.4254938066005707},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.4237637519836426},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3858543336391449},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3479721248149872},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.33854788541793823},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2025781273841858},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.11574029922485352},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.0863460898399353}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6947609186172485},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.629751443862915},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.5546379685401917},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5425101518630981},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.541970431804657},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5025451183319092},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.49855899810791016},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.49414733052253723},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.49383577704429626},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.44937658309936523},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4458332359790802},{"id":"https://openalex.org/C9810830","wikidata":"https://www.wikidata.org/wiki/Q635384","display_name":"Maximum a posteriori estimation","level":3,"score":0.4254938066005707},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.4237637519836426},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3858543336391449},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3479721248149872},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33854788541793823},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2025781273841858},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.11574029922485352},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0863460898399353},{"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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"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/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"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/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s10994-023-06491-x","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-023-06491-x","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-023-06491-x.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning","raw_type":"journal-article"},{"id":"pmh:oai:pure.ed.ac.uk:publications/12170492-e050-4122-b645-d890d6b4abf8","is_oa":true,"landing_page_url":"https://www.research.ed.ac.uk/en/publications/12170492-e050-4122-b645-d890d6b4abf8","pdf_url":null,"source":{"id":"https://openalex.org/S4406922455","display_name":"Edinburgh Research Explorer","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Etienam, C, Law, K, Wade, S K & Zankin, V 2024, 'Fast Deep Mixtures of Gaussian Process Experts', Machine Learning, vol. 113, no. 3, pp. 1483\u20131508. https://doi.org/10.1007/s10994-023-06491-x","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":{"id":"doi:10.1007/s10994-023-06491-x","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-023-06491-x","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-023-06491-x.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8813984943","display_name":null,"funder_award_id":"DE-AC05-00OR22725","funder_id":"https://openalex.org/F4320316892","funder_display_name":"UT-Battelle"}],"funders":[{"id":"https://openalex.org/F4320313467","display_name":"Alan Turing Institute","ror":"https://ror.org/035dkdb55"},{"id":"https://openalex.org/F4320316892","display_name":"UT-Battelle","ror":"https://ror.org/04nza6677"}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4390661296.pdf"},"referenced_works_count":66,"referenced_works":["https://openalex.org/W97653666","https://openalex.org/W137285897","https://openalex.org/W241901432","https://openalex.org/W1502922572","https://openalex.org/W1510444525","https://openalex.org/W1512149552","https://openalex.org/W1524392826","https://openalex.org/W1534349507","https://openalex.org/W1571870753","https://openalex.org/W1673310716","https://openalex.org/W1837874438","https://openalex.org/W1932198206","https://openalex.org/W1973310094","https://openalex.org/W1994683528","https://openalex.org/W2004807582","https://openalex.org/W2014101156","https://openalex.org/W2023405098","https://openalex.org/W2025653905","https://openalex.org/W2039497028","https://openalex.org/W2046996453","https://openalex.org/W2064977549","https://openalex.org/W2092271904","https://openalex.org/W2111683289","https://openalex.org/W2124386244","https://openalex.org/W2126292488","https://openalex.org/W2130879942","https://openalex.org/W2150884987","https://openalex.org/W2251230758","https://openalex.org/W2396665160","https://openalex.org/W2427628320","https://openalex.org/W2559655401","https://openalex.org/W2570630218","https://openalex.org/W2593379767","https://openalex.org/W2767640602","https://openalex.org/W2799211855","https://openalex.org/W2890056239","https://openalex.org/W2915071561","https://openalex.org/W2952558372","https://openalex.org/W2963464211","https://openalex.org/W2963625110","https://openalex.org/W2964024268","https://openalex.org/W2977466517","https://openalex.org/W2990394323","https://openalex.org/W2997084783","https://openalex.org/W3010999265","https://openalex.org/W3015040555","https://openalex.org/W3035427827","https://openalex.org/W3046273995","https://openalex.org/W4211049957","https://openalex.org/W4236654676","https://openalex.org/W6600774544","https://openalex.org/W6634817459","https://openalex.org/W6639216784","https://openalex.org/W6640000427","https://openalex.org/W6644682428","https://openalex.org/W6674468447","https://openalex.org/W6674660115","https://openalex.org/W6674989108","https://openalex.org/W6676245295","https://openalex.org/W6679512965","https://openalex.org/W6684770997","https://openalex.org/W6752481426","https://openalex.org/W6762710141","https://openalex.org/W6768732345","https://openalex.org/W6773969562","https://openalex.org/W6774576859"],"related_works":["https://openalex.org/W4388311650","https://openalex.org/W1974056099","https://openalex.org/W4287880334","https://openalex.org/W4245343541","https://openalex.org/W2150865841","https://openalex.org/W2138865713","https://openalex.org/W2169353922","https://openalex.org/W2100805585","https://openalex.org/W2169866437","https://openalex.org/W1964286703"],"abstract_inverted_index":{"Abstract":[0],"Mixtures":[1],"of":[2,27,70,103,131,161,199,202],"experts":[3,48,66,162,168,203],"have":[4,39],"become":[5],"an":[6],"indispensable":[7],"tool":[8],"for":[9,46,63,150],"flexible":[10],"modelling":[11],"in":[12,49,53,129,196],"a":[13,43,74,80,94,109,180],"supervised":[14],"learning":[15],"context,":[16],"allowing":[17],"not":[18,170],"only":[19],"the":[20,24,28,33,47,60,65,92,121,159,177,184,197],"mean":[21],"function":[22],"but":[23,146],"entire":[25],"density":[26],"output":[29],"to":[30,58,90,125,183],"change":[31],"with":[32],"inputs.":[34],"Sparse":[35],"Gaussian":[36],"processes":[37],"(GP)":[38],"shown":[40],"promise":[41],"as":[42],"leading":[44],"candidate":[45],"such":[50,68],"models,":[51],"and":[52,105,116,133,142,152,165],"this":[54],"article,":[55],"we":[56],"propose":[57],"design":[59],"gating":[61],"network":[62,77],"selecting":[64],"from":[67],"mixtures":[69],"sparse":[71],"GPs":[72],"using":[73],"deep":[75],"neural":[76],"(DNN).":[78],"Furthermore,":[79],"fast":[81],"one":[82],"pass":[83],"algorithm":[84,106,178],"called":[85],"Cluster\u2013Classify\u2013Regress":[86],"(CCR)":[87],"is":[88,113,123,138,147],"leveraged":[89],"approximate":[91],"maximum":[93],"posteriori":[95],"(MAP)":[96],"estimator":[97,187],"extremely":[98,117],"quickly.":[99],"This":[100,190],"powerful":[101],"combination":[102],"model":[104],"together":[107],"delivers":[108],"novel":[110],"method":[111,122],"which":[112,174],"flexible,":[114],"robust,":[115],"efficient.":[118],"In":[119],"particular,":[120],"able":[124],"outperform":[126],"competing":[127],"methods":[128],"terms":[130],"accuracy":[132],"uncertainty":[134],"quantification.":[135],"The":[136],"cost":[137],"competitive":[139],"on":[140],"low-dimensional":[141],"small":[143],"data":[144,154],"sets,":[145],"significantly":[148],"lower":[149],"higher-dimensional":[151],"big":[153],"sets":[155],".":[156],"Iteratively":[157],"maximizing":[158],"distribution":[160],"given":[163,167],"allocations":[164,166],"does":[169],"provide":[171],"significant":[172],"improvement,":[173],"indicates":[175],"that":[176],"achieves":[179],"good":[181],"approximation":[182],"local":[185],"MAP":[186],"very":[188],"fast.":[189],"insight":[191],"can":[192],"be":[193],"useful":[194],"also":[195],"context":[198],"other":[200],"mixture":[201],"models.":[204]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1}],"updated_date":"2026-05-08T15:41:06.802602","created_date":"2025-10-10T00:00:00"}