{"id":"https://openalex.org/W7162696464","doi":"https://doi.org/10.1016/j.neucom.2026.134122","title":"Gaussian process regression with transfer learning under distribution shift: A joint probability neural kernel approach","display_name":"Gaussian process regression with transfer learning under distribution shift: A joint probability neural kernel approach","publication_year":2026,"publication_date":"2026-05-28","ids":{"openalex":"https://openalex.org/W7162696464","doi":"https://doi.org/10.1016/j.neucom.2026.134122"},"language":"en","primary_location":{"id":"doi:10.1016/j.neucom.2026.134122","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.neucom.2026.134122","pdf_url":null,"source":{"id":"https://openalex.org/S45693802","display_name":"Neurocomputing","issn_l":"0925-2312","issn":["0925-2312","1872-8286"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neurocomputing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1016/j.neucom.2026.134122","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137218808","display_name":"Ruiwen Cao","orcid":null},"institutions":[{"id":"https://openalex.org/I153648349","display_name":"University of Leicester","ror":"https://ror.org/04h699437","country_code":"GB","type":"education","lineage":["https://openalex.org/I153648349"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Ruiwen Cao","raw_affiliation_strings":["School of Computing and Mathematical Sciences, University of Leicester, University Road, Leicester, LE1 7RH, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computing and Mathematical Sciences, University of Leicester, University Road, Leicester, LE1 7RH, UK","institution_ids":["https://openalex.org/I153648349"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137275743","display_name":"Bo Wang","orcid":"https://orcid.org/0000-0001-6779-1933"},"institutions":[{"id":"https://openalex.org/I153648349","display_name":"University of Leicester","ror":"https://ror.org/04h699437","country_code":"GB","type":"education","lineage":["https://openalex.org/I153648349"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Bo Wang","raw_affiliation_strings":["School of Computing and Mathematical Sciences, University of Leicester, University Road, Leicester, LE1 7RH, UK"],"raw_orcid":"https://orcid.org/0000-0001-6779-1933","affiliations":[{"raw_affiliation_string":"School of Computing and Mathematical Sciences, University of Leicester, University Road, Leicester, LE1 7RH, UK","institution_ids":["https://openalex.org/I153648349"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073325171","display_name":"Neslihan S\u00fczen","orcid":null},"institutions":[{"id":"https://openalex.org/I153648349","display_name":"University of Leicester","ror":"https://ror.org/04h699437","country_code":"GB","type":"education","lineage":["https://openalex.org/I153648349"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Neslihan Suzen","raw_affiliation_strings":["School of Computing and Mathematical Sciences, University of Leicester, University Road, Leicester, LE1 7RH, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computing and Mathematical Sciences, University of Leicester, University Road, Leicester, LE1 7RH, UK","institution_ids":["https://openalex.org/I153648349"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5137218808"],"corresponding_institution_ids":["https://openalex.org/I153648349"],"apc_list":{"value":2470,"currency":"USD","value_usd":2470},"apc_paid":{"value":2470,"currency":"USD","value_usd":2470},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.66623132,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"696","issue":null,"first_page":"134122","last_page":"134122"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9735999703407288,"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":0.9735999703407288,"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/T12676","display_name":"Machine Learning and ELM","score":0.007300000172108412,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.0034000000450760126,"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/joint-probability-distribution","display_name":"Joint probability distribution","score":0.5938000082969666},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5360000133514404},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.5177000164985657},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5164999961853027},{"id":"https://openalex.org/keywords/kernel-regression","display_name":"Kernel regression","score":0.46700000762939453},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.46560001373291016},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.4645000100135803},{"id":"https://openalex.org/keywords/probability-distribution","display_name":"Probability distribution","score":0.46309998631477356},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.41350001096725464}],"concepts":[{"id":"https://openalex.org/C18653775","wikidata":"https://www.wikidata.org/wiki/Q1333358","display_name":"Joint probability distribution","level":2,"score":0.5938000082969666},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5925999879837036},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5688999891281128},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5360000133514404},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.5177000164985657},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5164999961853027},{"id":"https://openalex.org/C200695384","wikidata":"https://www.wikidata.org/wiki/Q1739319","display_name":"Kernel regression","level":3,"score":0.46700000762939453},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.46560001373291016},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.4645000100135803},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.46309998631477356},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.41350001096725464},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.4099999964237213},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.40779998898506165},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.398499995470047},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3725000023841858},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.35040000081062317},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3447999954223633},{"id":"https://openalex.org/C71134354","wikidata":"https://www.wikidata.org/wiki/Q458825","display_name":"Kernel density estimation","level":3,"score":0.33079999685287476},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3138999938964844},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.31119999289512634},{"id":"https://openalex.org/C2776175482","wikidata":"https://www.wikidata.org/wiki/Q1195816","display_name":"Transfer (computing)","level":2,"score":0.30489999055862427},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.29820001125335693},{"id":"https://openalex.org/C81692654","wikidata":"https://www.wikidata.org/wiki/Q225926","display_name":"Kriging","level":2,"score":0.29159998893737793},{"id":"https://openalex.org/C197055811","wikidata":"https://www.wikidata.org/wiki/Q207522","display_name":"Probability density function","level":2,"score":0.2888000011444092},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.2856000065803528},{"id":"https://openalex.org/C134517425","wikidata":"https://www.wikidata.org/wiki/Q16000131","display_name":"Kernel embedding of distributions","level":4,"score":0.25920000672340393},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.2581000030040741},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.2574000060558319},{"id":"https://openalex.org/C74887250","wikidata":"https://www.wikidata.org/wiki/Q3455892","display_name":"Principal component regression","level":3,"score":0.25290000438690186},{"id":"https://openalex.org/C182335926","wikidata":"https://www.wikidata.org/wiki/Q17093020","display_name":"Kernel principal component analysis","level":4,"score":0.25279998779296875}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1016/j.neucom.2026.134122","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.neucom.2026.134122","pdf_url":null,"source":{"id":"https://openalex.org/S45693802","display_name":"Neurocomputing","issn_l":"0925-2312","issn":["0925-2312","1872-8286"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neurocomputing","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1016/j.neucom.2026.134122","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.neucom.2026.134122","pdf_url":null,"source":{"id":"https://openalex.org/S45693802","display_name":"Neurocomputing","issn_l":"0925-2312","issn":["0925-2312","1872-8286"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neurocomputing","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320322725","display_name":"China Scholarship Council","ror":"https://ror.org/04atp4p48"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W1898904249","https://openalex.org/W2159570078","https://openalex.org/W2213174481","https://openalex.org/W2326887180","https://openalex.org/W2558411832","https://openalex.org/W4280544476","https://openalex.org/W4312667407"],"related_works":[],"abstract_inverted_index":{"Gaussian":[0],"Process":[1],"Regression":[2],"(GPR)":[3],"is":[4,45],"a":[5,92,165,181,234],"powerful":[6],"non-parametric":[7],"statistical":[8],"modelling":[9],"approach,":[10],"widely":[11],"valued":[12],"for":[13,189,203,237],"its":[14,21],"flexibility":[15],"and":[16,102,177,214,229,232,244],"strong":[17],"uncertainty":[18],"quantification.":[19],"However,":[20],"predictive":[22],"performance":[23,142],"can":[24],"deteriorate":[25],"substantially":[26],"in":[27,99,224],"data-scarce":[28],"settings,":[29],"such":[30],"as":[31,164],"applications":[32],"involving":[33],"expensive":[34],"or":[35],"time-intensive":[36],"data":[37,184],"collection.":[38],"A":[39],"promising":[40],"solution":[41,236],"to":[42,61,96,171,174],"this":[43,113],"problem":[44],"the":[46,58,82,87,150,169,198],"integration":[47],"of":[48,135,226],"transfer":[49,74],"learning":[50,76],"(TL)":[51],"with":[52,68,119,159,240],"GPR,":[53],"which":[54,124,138],"leverages":[55],"knowledge":[56],"from":[57],"source":[59,88,151],"task":[60,64],"enhance":[62],"target":[63,242],"predictions.":[65],"Existing":[66],"GPR":[67,118],"TL":[69,120],"methods":[70,223],"based":[71],"on":[72,106,149,205,211],"adaptive":[73],"kernel":[75],"have":[77],"several":[78],"limitations,":[79],"including:":[80],"optimising":[81],"conditional":[83,136],"probability":[84,131],"while":[85,143,192],"ignoring":[86],"domain":[89,245],"distribution,":[90],"lacking":[91],"shift":[93],"correction":[94],"mechanism":[95],"address":[97,110],"shifts":[98],"feature":[100],"distributions,":[101],"high":[103],"computational":[104,187,230],"complexity":[105],"large-scale":[107,206],"datasets.":[108,207],"To":[109],"these":[111],"issues,":[112],"paper":[114],"proposes":[115],"an":[116],"efficient":[117,202],"model,":[121],"namely":[122],"JPNKN,":[123],"has":[125],"three":[126],"distinctive":[127],"advantages:":[128],"(1)":[129],"joint":[130],"likelihood":[132],"maximization":[133],"instead":[134],"probability,":[137],"improves":[139],"target-domain":[140],"prediction":[141,227,238],"simultaneously":[144],"preserving":[145,193],"accurate":[146],"inference":[147],"capability":[148],"domain;":[152],"(2)":[153],"multi-layer":[154],"Neural":[155],"Kernel":[156],"Network":[157],"(NKN),":[158],"Maximum":[160],"Mean":[161],"Discrepancy":[162],"(MMD)":[163],"regularization":[166],"term,":[167],"enabling":[168],"model":[170,200],"better":[172],"adapt":[173],"nonlinear":[175],"relationships":[176],"distribution":[178,195],"shifts;":[179],"(3)":[180],"novel":[182],"Wasserstein-based":[183],"distillation,":[185],"reducing":[186],"time":[188],"NKN":[190],"training":[191],"source-domain":[194],"characteristics,":[196],"making":[197],"JPNKN":[199,219],"more":[201],"deployment":[204],"Extensive":[208],"experimental":[209],"results":[210],"both":[212],"synthetic":[213],"real-world":[215],"datasets":[216],"demonstrate":[217],"that":[218],"significantly":[220],"outperforms":[221],"existing":[222],"terms":[225],"accuracy":[228],"efficiency,":[231],"provides":[233],"new":[235],"tasks":[239],"scarce":[241],"samples":[243],"shift.":[246]},"counts_by_year":[],"updated_date":"2026-06-03T06:16:58.514037","created_date":"2026-05-29T00:00:00"}