{"id":"https://openalex.org/W2148522164","doi":"https://doi.org/10.1145/1102351.1102479","title":"Learning Gaussian processes from multiple tasks","display_name":"Learning Gaussian processes from multiple tasks","publication_year":2005,"publication_date":"2005-01-01","ids":{"openalex":"https://openalex.org/W2148522164","doi":"https://doi.org/10.1145/1102351.1102479","mag":"2148522164"},"language":"en","primary_location":{"id":"doi:10.1145/1102351.1102479","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1102351.1102479","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 22nd international conference on Machine learning  - ICML '05","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/A5100692615","display_name":"Kai Yu","orcid":"https://orcid.org/0000-0001-6593-2130"},"institutions":[{"id":"https://openalex.org/I1325886976","display_name":"Siemens (Germany)","ror":"https://ror.org/059mq0909","country_code":"DE","type":"company","lineage":["https://openalex.org/I1325886976"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Kai Yu","raw_affiliation_strings":["Corporate Technology, Siemens AG, Munich, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Corporate Technology, Siemens AG, Munich, Germany","institution_ids":["https://openalex.org/I1325886976"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074808403","display_name":"Volker Tresp","orcid":"https://orcid.org/0000-0001-9428-3686"},"institutions":[{"id":"https://openalex.org/I1325886976","display_name":"Siemens (Germany)","ror":"https://ror.org/059mq0909","country_code":"DE","type":"company","lineage":["https://openalex.org/I1325886976"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Volker Tresp","raw_affiliation_strings":["Corporate Technology, Siemens AG, Munich, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Corporate Technology, Siemens AG, Munich, Germany","institution_ids":["https://openalex.org/I1325886976"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010707054","display_name":"Anton Schwaighofer","orcid":"https://orcid.org/0000-0003-1557-0527"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Anton Schwaighofer","raw_affiliation_strings":["Intelligent Data Analysis Group, Fraunhofer FIRST, Berlin"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Intelligent Data Analysis Group, Fraunhofer FIRST, Berlin","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":21.1339,"has_fulltext":false,"cited_by_count":391,"citation_normalized_percentile":{"value":0.99414934,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1012","last_page":"1019"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9958999752998352,"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":0.9958999752998352,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9757000207901001,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9739999771118164,"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/computer-science","display_name":"Computer science","score":0.7603753805160522},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.6207990050315857},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5845949649810791},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5828790068626404},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5595883131027222},{"id":"https://openalex.org/keywords/categorization","display_name":"Categorization","score":0.5578088164329529},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5304961204528809},{"id":"https://openalex.org/keywords/multi-task-learning","display_name":"Multi-task learning","score":0.5282102227210999},{"id":"https://openalex.org/keywords/nonparametric-statistics","display_name":"Nonparametric statistics","score":0.5136420130729675},{"id":"https://openalex.org/keywords/equivalence","display_name":"Equivalence (formal languages)","score":0.4987795352935791},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.4745279848575592},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4719223976135254},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.46466219425201416},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16781103610992432},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.07862991094589233},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.07786768674850464}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7603753805160522},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.6207990050315857},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5845949649810791},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5828790068626404},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5595883131027222},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.5578088164329529},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5304961204528809},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.5282102227210999},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.5136420130729675},{"id":"https://openalex.org/C2780069185","wikidata":"https://www.wikidata.org/wiki/Q7977945","display_name":"Equivalence (formal languages)","level":2,"score":0.4987795352935791},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.4745279848575592},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4719223976135254},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.46466219425201416},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16781103610992432},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.07862991094589233},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.07786768674850464},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.1145/1102351.1102479","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1102351.1102479","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 22nd international conference on Machine learning  - ICML '05","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.66.9029","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.66.9029","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://tresp.org/papers/multitaskGP_final.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.69.6971","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.69.6971","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.dbs.informatik.uni-muenchen.de/~yu_k/icml2005_1.pdf","raw_type":"text"},{"id":"pmh:oai:fraunhofer.de:N-55494","is_oa":false,"landing_page_url":"http://publica.fraunhofer.de/documents/N-55494.html","pdf_url":null,"source":{"id":"https://openalex.org/S4306400801","display_name":"Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4923324","host_organization_name":"Fraunhofer-Gesellschaft","host_organization_lineage":["https://openalex.org/I4923324"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Fraunhofer FIRST","raw_type":"Conference Paper"},{"id":"pmh:oai:publica.fraunhofer.de:publica/349981","is_oa":false,"landing_page_url":"https://publica.fraunhofer.de/handle/publica/349981","pdf_url":null,"source":{"id":"https://openalex.org/S4306400318","display_name":"Fraunhofer-Publica (Fraunhofer-Gesellschaft)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4923324","host_organization_name":"Fraunhofer-Gesellschaft","host_organization_lineage":["https://openalex.org/I4923324"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"conference paper"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W66185676","https://openalex.org/W1524688041","https://openalex.org/W1657213141","https://openalex.org/W1746680969","https://openalex.org/W1880262756","https://openalex.org/W2030290736","https://openalex.org/W2045656233","https://openalex.org/W2098385651","https://openalex.org/W2130903752","https://openalex.org/W2143104527","https://openalex.org/W2149684865","https://openalex.org/W2150102617","https://openalex.org/W2914746235","https://openalex.org/W3148198191","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W2165912799","https://openalex.org/W2735662278","https://openalex.org/W2382615723","https://openalex.org/W4311804456","https://openalex.org/W1987484445","https://openalex.org/W2623658258","https://openalex.org/W2143413548","https://openalex.org/W2185334388","https://openalex.org/W4308090169","https://openalex.org/W1972390760"],"abstract_inverted_index":{"We":[0],"consider":[1],"the":[2,24,53],"problem":[3],"of":[4,59],"multi-task":[5,61],"learning,":[6],"that":[7,22,52],"is,":[8],"learning":[9],"multiple":[10],"related":[11],"functions.":[12],"Our":[13],"approach":[14],"is":[15],"based":[16],"on":[17,47],"a":[18],"hierarchical":[19],"Bayesian":[20],"framework,":[21],"exploits":[23],"equivalence":[25],"between":[26],"parametric":[27],"linear":[28],"models":[29,37,55],"and":[30],"nonparametric":[31],"Gaussian":[32],"processes":[33],"(GPs).":[34],"The":[35],"resulting":[36],"can":[38],"be":[39],"learned":[40],"easily":[41],"via":[42],"an":[43],"EM-algorithm.":[44],"Empirical":[45],"studies":[46],"multi-label":[48],"text":[49],"categorization":[50],"suggest":[51],"presented":[54],"allow":[56],"accurate":[57],"solutions":[58],"these":[60],"problems.":[62]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":19},{"year":2020,"cited_by_count":23},{"year":2019,"cited_by_count":15},{"year":2018,"cited_by_count":29},{"year":2017,"cited_by_count":35},{"year":2016,"cited_by_count":22},{"year":2015,"cited_by_count":32},{"year":2014,"cited_by_count":23},{"year":2013,"cited_by_count":18},{"year":2012,"cited_by_count":32}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}