{"id":"https://openalex.org/W2139259843","doi":null,"title":"It is all in the noise: Efficient multi-task Gaussian process inference with structured residuals","display_name":"It is all in the noise: Efficient multi-task Gaussian process inference with structured residuals","publication_year":2013,"publication_date":"2013-12-05","ids":{"openalex":"https://openalex.org/W2139259843","mag":"2139259843"},"language":"en","primary_location":{"id":"pmh:oai:escidoc.org:escidoc:1922321","doi":null,"is_oa":false,"landing_page_url":"http://hdl.handle.net/11858/00-001M-0000-0015-3A26-A","pdf_url":null,"source":{"id":"https://openalex.org/S7407052962","display_name":"Max Planck Digital Library","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I4210088280","host_organization_name":"Max Planck Digital Library","host_organization_lineage":["https://openalex.org/I4210088280"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/conferenceObject"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":[],"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/A5036827432","display_name":"Barbara Rakitsch","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210165766","display_name":"Max Planck Institute for Biology","ror":"https://ror.org/0243gzr89","country_code":"DE","type":"facility","lineage":["https://openalex.org/I149899117","https://openalex.org/I4210165766"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Barbara Rakitsch","raw_affiliation_strings":["Machine Learning and Computational Biology Research Group, Max Planck Institutes, T\u00fcbingen, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Machine Learning and Computational Biology Research Group, Max Planck Institutes, T\u00fcbingen, Germany","institution_ids":["https://openalex.org/I4210165766"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074204533","display_name":"Christoph Lippert","orcid":"https://orcid.org/0000-0001-6363-2556","observed_orcids":["https://orcid.org/0000-0001-6363-2556"]},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Christoph Lippert","raw_affiliation_strings":["Microsoft Research, Los Angeles#TAB#"],"raw_orcid":"https://orcid.org/0000-0001-6363-2556","affiliations":[{"raw_affiliation_string":"Microsoft Research, Los Angeles#TAB#","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074540538","display_name":"Karsten Michael Borgwardt","orcid":"https://orcid.org/0000-0001-7221-2393","observed_orcids":["https://orcid.org/0000-0001-7221-2393"]},"institutions":[{"id":"https://openalex.org/I149899117","display_name":"Max Planck Society","ror":"https://ror.org/01hhn8329","country_code":"DE","type":"nonprofit","lineage":["https://openalex.org/I149899117"]},{"id":"https://openalex.org/I8087733","display_name":"University of T\u00fcbingen","ror":"https://ror.org/03a1kwz48","country_code":"DE","type":"education","lineage":["https://openalex.org/I8087733"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Karsten Borgwardt","raw_affiliation_strings":["Machine Learning and Computational Biology, Research Group, Max Planck Institutes, T\u00fcbingen, Germany and Zentrum f\u00fcr Bioinformatik, Eberhard Karls Universit\u00e4t T\u00fcbingen, T\u00fc ...#TAB#"],"raw_orcid":"https://orcid.org/0000-0001-7221-2393","affiliations":[{"raw_affiliation_string":"Machine Learning and Computational Biology, Research Group, Max Planck Institutes, T\u00fcbingen, Germany and Zentrum f\u00fcr Bioinformatik, Eberhard Karls Universit\u00e4t T\u00fcbingen, T\u00fc ...#TAB#","institution_ids":["https://openalex.org/I149899117","https://openalex.org/I8087733"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5091002491","display_name":"Oliver Stegle","orcid":"https://orcid.org/0000-0002-8818-7193","observed_orcids":["https://orcid.org/0000-0002-8818-7193","https://orcid.org/0000-0003-4124-2375"]},"institutions":[{"id":"https://openalex.org/I1303153112","display_name":"European Bioinformatics Institute","ror":"https://ror.org/02catss52","country_code":"GB","type":"facility","lineage":["https://openalex.org/I1303153112","https://openalex.org/I4210138560"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Oliver Stegle","raw_affiliation_strings":["European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK"],"raw_orcid":"https://orcid.org/0000-0002-8818-7193","affiliations":[{"raw_affiliation_string":"European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK","institution_ids":["https://openalex.org/I1303153112"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.3597,"has_fulltext":false,"cited_by_count":51,"citation_normalized_percentile":{"value":0.82022964,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":99},"biblio":{"volume":"26","issue":null,"first_page":"1466","last_page":"1474"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9994999766349792,"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":0.9994999766349792,"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/T10320","display_name":"Neural Networks and Applications","score":0.0,"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/T11911","display_name":"Spatial and Panel Data Analysis","score":0.0,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/multi-task-gaussian-process","display_name":"multi-task Gaussian process","score":0.847000002861023},{"id":"https://openalex.org/keywords/structured-residuals","display_name":"structured residuals","score":0.8489999771118164},{"id":"https://openalex.org/keywords/task-correlation","display_name":"task correlation","score":0.5410000085830688},{"id":"https://openalex.org/keywords/parameter-inference","display_name":"parameter inference","score":0.609000027179718},{"id":"https://openalex.org/keywords/out-of-sample-forecasting","display_name":"out-of-sample forecasting","score":0.9900000095367432},{"id":"https://openalex.org/keywords/phenotype-prediction","display_name":"phenotype prediction","score":0.6499999761581421},{"id":"https://openalex.org/keywords/genetic-models","display_name":"genetic models","score":0.45100000500679016},{"id":"https://openalex.org/keywords/noise-modeling","display_name":"noise modeling","score":0.4729999899864197}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6927714943885803},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6620903611183167},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.6258957386016846},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.596611738204956},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5877822637557983},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5653617978096008},{"id":"https://openalex.org/C39482219","wikidata":"https://www.wikidata.org/wiki/Q192826","display_name":"Kronecker delta","level":2,"score":0.520375669002533},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5189816355705261},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.46814969182014465},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.4638208746910095},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45544907450675964},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4417259395122528},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.42009907960891724},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36987483501434326},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3535761833190918},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.25493699312210083},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.22439539432525635},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09583878517150879},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","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":3,"locations":[{"id":"pmh:oai:escidoc.org:escidoc:1922321","doi":null,"is_oa":false,"landing_page_url":"http://hdl.handle.net/11858/00-001M-0000-0015-3A26-A","pdf_url":null,"source":{"id":"https://openalex.org/S7407052962","display_name":"Max Planck Digital Library","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I4210088280","host_organization_name":"Max Planck Digital Library","host_organization_lineage":["https://openalex.org/I4210088280"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/conferenceObject"},{"id":"pmh:oai:pure.mpg.de:item_1922321","doi":null,"is_oa":false,"landing_page_url":"http://hdl.handle.net/11858/00-001M-0000-0015-3A26-A","pdf_url":null,"source":{"id":"https://openalex.org/S4306400654","display_name":"MPG.PuRe (Max Planck Society)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I149899117","host_organization_name":"Max Planck Society","host_organization_lineage":["https://openalex.org/I149899117"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Advances in Neural Information Processing Systems 26 (NIPS 2013)","raw_type":"info:eu-repo/semantics/conferenceObject"},{"id":"mag:2139259843","doi":null,"is_oa":false,"landing_page_url":"https://papers.nips.cc/paper/5089-it-is-all-in-the-noise-efficient-multi-task-gaussian-process-inference-with-structured-residuals.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4363606243","display_name":"neural information processing systems","issn_l":"1049-5258","issn":["1049-5258"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-2"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Neural Information Processing Systems","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1519342765","https://openalex.org/W1545951971","https://openalex.org/W1746819321","https://openalex.org/W2003747371","https://openalex.org/W2005126631","https://openalex.org/W2038225430","https://openalex.org/W2070500827","https://openalex.org/W2082509948","https://openalex.org/W2119595900","https://openalex.org/W2134833137","https://openalex.org/W2137797806","https://openalex.org/W2143672530","https://openalex.org/W2157380881","https://openalex.org/W2158170196","https://openalex.org/W2396659250"],"related_works":["https://openalex.org/W2049927789","https://openalex.org/W2119595900","https://openalex.org/W785831587","https://openalex.org/W2105869342","https://openalex.org/W7100078043","https://openalex.org/W7026066580","https://openalex.org/W2099604794","https://openalex.org/W7097905137","https://openalex.org/W4389095108","https://openalex.org/W2112005937"],"abstract_inverted_index":{"Multi-task":[0],"prediction":[1,74,85],"methods":[2],"are":[3,75],"widely":[4],"used":[5],"to":[6,41,83,97],"couple":[7],"regressors":[8,31],"or":[9],"classification":[10],"models":[11],"by":[12],"sharing":[13,46],"information":[14],"across":[15],"related":[16],"tasks.":[17],"We":[18],"propose":[19],"a":[20,54,60],"multi-task":[21],"Gaussian":[22,51],"process":[23],"approach":[24],"for":[25,65],"modeling":[26,93],"both":[27,78],"the":[28,33,37],"relatedness":[29],"between":[30,47],"and":[32,70,81],"task":[34],"correlations":[35],"in":[36,39,57,86],"residuals,":[38],"order":[40],"more":[42],"accurately":[43],"identify":[44],"true":[45],"regressors.":[48],"The":[49],"resulting":[50],"model":[52],"has":[53],"covariance":[55],"term":[56],"form":[58],"of":[59,62,72,92],"sum":[61],"Kronecker":[63],"products,":[64],"which":[66],"efficient":[67],"parameter":[68],"inference":[69],"out":[71],"sample":[73],"feasible.":[76],"On":[77],"synthetic":[79],"examples":[80],"applications":[82],"phenotype":[84],"genetics,":[87],"we":[88],"find":[89],"substantial":[90],"benefits":[91],"structured":[94],"noise":[95],"compared":[96],"established":[98],"alternatives.":[99]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":11},{"year":2017,"cited_by_count":6},{"year":2016,"cited_by_count":4},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":1}],"updated_date":"2026-11-05T12:25:35.769452","created_date":"2016-06-24T00:00:00"}
