{"id":"https://openalex.org/W2915550698","doi":"https://doi.org/10.24963/ijcai.2019/271","title":"Human-in-the-loop Active Covariance Learning for Improving Prediction in Small Data Sets","display_name":"Human-in-the-loop Active Covariance Learning for Improving Prediction in Small Data Sets","publication_year":2019,"publication_date":"2019-07-28","ids":{"openalex":"https://openalex.org/W2915550698","doi":"https://doi.org/10.24963/ijcai.2019/271","mag":"2915550698"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2019/271","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2019/271","pdf_url":"https://www.ijcai.org/proceedings/2019/0271.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.ijcai.org/proceedings/2019/0271.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5000594867","display_name":"Homayun Afrabandpey","orcid":"https://orcid.org/0000-0001-7639-1501"},"institutions":[{"id":"https://openalex.org/I32943570","display_name":"Helsinki Institute for Information Technology","ror":"https://ror.org/05kph4940","country_code":"FI","type":"facility","lineage":["https://openalex.org/I133731052","https://openalex.org/I32943570","https://openalex.org/I9927081"]},{"id":"https://openalex.org/I9927081","display_name":"Aalto University","ror":"https://ror.org/020hwjq30","country_code":"FI","type":"education","lineage":["https://openalex.org/I9927081"]}],"countries":["FI"],"is_corresponding":true,"raw_author_name":"Homayun Afrabandpey","raw_affiliation_strings":["Department of Computer Science, Aalto University","Helsinki Institute for Information Technology HIIT,"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Aalto University","institution_ids":["https://openalex.org/I9927081"]},{"raw_affiliation_string":"Helsinki Institute for Information Technology HIIT,","institution_ids":["https://openalex.org/I32943570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103819361","display_name":"Tomi Peltola","orcid":null},"institutions":[{"id":"https://openalex.org/I9927081","display_name":"Aalto University","ror":"https://ror.org/020hwjq30","country_code":"FI","type":"education","lineage":["https://openalex.org/I9927081"]},{"id":"https://openalex.org/I32943570","display_name":"Helsinki Institute for Information Technology","ror":"https://ror.org/05kph4940","country_code":"FI","type":"facility","lineage":["https://openalex.org/I133731052","https://openalex.org/I32943570","https://openalex.org/I9927081"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Tomi Peltola","raw_affiliation_strings":["Department of Computer Science, Aalto University","Helsinki Institute for Information Technology HIIT,"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Aalto University","institution_ids":["https://openalex.org/I9927081"]},{"raw_affiliation_string":"Helsinki Institute for Information Technology HIIT,","institution_ids":["https://openalex.org/I32943570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018305257","display_name":"Samuel Kaski","orcid":"https://orcid.org/0000-0003-1925-9154"},"institutions":[{"id":"https://openalex.org/I32943570","display_name":"Helsinki Institute for Information Technology","ror":"https://ror.org/05kph4940","country_code":"FI","type":"facility","lineage":["https://openalex.org/I133731052","https://openalex.org/I32943570","https://openalex.org/I9927081"]},{"id":"https://openalex.org/I9927081","display_name":"Aalto University","ror":"https://ror.org/020hwjq30","country_code":"FI","type":"education","lineage":["https://openalex.org/I9927081"]},{"id":"https://openalex.org/I4210134318","display_name":"University of Technology","ror":"https://ror.org/03gbw6p94","country_code":"RU","type":"education","lineage":["https://openalex.org/I4210134318"]}],"countries":["FI","RU"],"is_corresponding":false,"raw_author_name":"Samuel Kaski","raw_affiliation_strings":["Department of Computer Science, Aalto University","Helsinki Institute for Information Technology HIIT,","(Helsinki University of Technology)"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Aalto University","institution_ids":["https://openalex.org/I9927081"]},{"raw_affiliation_string":"Helsinki Institute for Information Technology HIIT,","institution_ids":["https://openalex.org/I32943570"]},{"raw_affiliation_string":"(Helsinki University of Technology)","institution_ids":["https://openalex.org/I4210134318"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5000594867"],"corresponding_institution_ids":["https://openalex.org/I32943570","https://openalex.org/I9927081"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.01498142,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1959","last_page":"1966"},"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":0.9955000281333923,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9932000041007996,"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":0.7023458480834961},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.6575242280960083},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.646088719367981},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6133428812026978},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5398205518722534},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5333079695701599},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.47780662775039673},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4686996638774872},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.4388251304626465},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.2981785535812378},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.21218547224998474},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.20840099453926086}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7023458480834961},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.6575242280960083},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.646088719367981},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6133428812026978},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5398205518722534},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5333079695701599},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.47780662775039673},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4686996638774872},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.4388251304626465},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.2981785535812378},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.21218547224998474},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.20840099453926086},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":6,"locations":[{"id":"doi:10.24963/ijcai.2019/271","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2019/271","pdf_url":"https://www.ijcai.org/proceedings/2019/0271.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.atira.dk:openaire_cris_publications/ed8a1ce3-7588-48aa-8a41-da8682f5d467","is_oa":false,"landing_page_url":"https://research.manchester.ac.uk/en/publications/ed8a1ce3-7588-48aa-8a41-da8682f5d467","pdf_url":null,"source":{"id":"https://openalex.org/S4306400662","display_name":"Research Explorer (The University of Manchester)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I28407311","host_organization_name":"University of Manchester","host_organization_lineage":["https://openalex.org/I28407311"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Afrabandpey, H, Peltola, T & Kaski, S 2019, Human-in-the-loop Active Covariance Learning for Improving Prediction in Small Data Sets. in Proceedings of IJCAI 2019, the Twenty-Eighth International Joint Conference on Artificial Intelligence. pp. 1959-1966. < https://www.ijcai.org/proceedings/2019/0271.pdf >","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:oai:pure.atira.dk:publications/2d53c885-52fc-488d-8fbd-013b0d2564e4","is_oa":false,"landing_page_url":"https://research.manchester.ac.uk/en/publications/2d53c885-52fc-488d-8fbd-013b0d2564e4","pdf_url":null,"source":{"id":"https://openalex.org/S4306400662","display_name":"Research Explorer (The University of Manchester)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I28407311","host_organization_name":"University of Manchester","host_organization_lineage":["https://openalex.org/I28407311"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Afrabandpey, H, Peltola, T & Kaski, S 2019, Human-in-the-loop Active Covariance Learning for Improving Prediction in Small Data Sets. in Proceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019. IJCAI Organization, pp. 1959-1966, International Joint Conference on Artificial Intelligence, China, 10/08/19. https://doi.org/10.24963/ijcai.2019/271","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:oai:arXiv.org:1902.09834","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1902.09834","pdf_url":"https://arxiv.org/pdf/1902.09834","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"},{"id":"mag:2915550698","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1902.09834.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.1902.09834","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1902.09834","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2019/271","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2019/271","pdf_url":"https://www.ijcai.org/proceedings/2019/0271.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.5600000023841858,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[{"id":"https://openalex.org/G1248897035","display_name":null,"funder_award_id":"Vilho","funder_id":"https://openalex.org/F4320321108","funder_display_name":"Academy of Finland"},{"id":"https://openalex.org/G1305559923","display_name":null,"funder_award_id":"Vilho","funder_id":"https://openalex.org/F4320321109","funder_display_name":"Suomalainen Tiedeakatemia"},{"id":"https://openalex.org/G1880223270","display_name":null,"funder_award_id":"313195","funder_id":"https://openalex.org/F4320321108","funder_display_name":"Academy of Finland"},{"id":"https://openalex.org/G5498917553","display_name":null,"funder_award_id":"294238","funder_id":"https://openalex.org/F4320321108","funder_display_name":"Academy of Finland"},{"id":"https://openalex.org/G689928359","display_name":null,"funder_award_id":"319264","funder_id":"https://openalex.org/F4320321108","funder_display_name":"Academy of Finland"}],"funders":[{"id":"https://openalex.org/F4320321108","display_name":"Academy of Finland","ror":"https://ror.org/05k73zm37"},{"id":"https://openalex.org/F4320321109","display_name":"Suomalainen Tiedeakatemia","ror":"https://ror.org/01njdr168"},{"id":"https://openalex.org/F4320321394","display_name":"Aalto-Yliopisto","ror":"https://ror.org/020hwjq30"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2915550698.pdf","grobid_xml":"https://content.openalex.org/works/W2915550698.grobid-xml"},"referenced_works_count":20,"referenced_works":["https://openalex.org/W1523884323","https://openalex.org/W1538776335","https://openalex.org/W1557757161","https://openalex.org/W1746819321","https://openalex.org/W1957430426","https://openalex.org/W1980108910","https://openalex.org/W2004503204","https://openalex.org/W2010441486","https://openalex.org/W2078089550","https://openalex.org/W2097360283","https://openalex.org/W2153579005","https://openalex.org/W2163302275","https://openalex.org/W2166471851","https://openalex.org/W2170073305","https://openalex.org/W2563271136","https://openalex.org/W2577537660","https://openalex.org/W2613206385","https://openalex.org/W2792853973","https://openalex.org/W3098967121","https://openalex.org/W3100372030"],"related_works":["https://openalex.org/W2964696683","https://openalex.org/W2397002046","https://openalex.org/W2939002775","https://openalex.org/W3118640283","https://openalex.org/W3098636317","https://openalex.org/W2226403400","https://openalex.org/W2140217547","https://openalex.org/W940094962","https://openalex.org/W2618438111","https://openalex.org/W2899306510","https://openalex.org/W3086127638","https://openalex.org/W1493197711","https://openalex.org/W3040007568","https://openalex.org/W1544165511","https://openalex.org/W655118345","https://openalex.org/W3112138688","https://openalex.org/W1511670714","https://openalex.org/W2949377985","https://openalex.org/W3095513820","https://openalex.org/W1983429559"],"abstract_inverted_index":{"Learning":[0],"predictive":[1,117],"models":[2],"from":[3],"small":[4],"high-dimensional":[5,13,126],"data":[6],"sets":[7],"is":[8,42],"a":[9,21,69,137],"key":[10],"problem":[11],"in":[12,96,116,125],"statistics.":[14],"Expert":[15],"knowledge":[16,87],"elicitation":[17],"can":[18],"help,":[19],"and":[20,51,76,99,122],"strong":[22],"line":[23,59],"of":[24,55,60,65,109],"work":[25,61],"focuses":[26],"on":[27,49,53,119,141],"directly":[28],"eliciting":[29,85],"informative":[30],"prior":[31],"distributions":[32],"for":[33],"parameters.":[34],"This":[35],"either":[36],"requires":[37],"considerable":[38],"statistical":[39,94],"expertise":[40],"or":[41],"laborious,":[43],"as":[44],"the":[45,56,97,107,110,133],"emphasis":[46],"has":[47],"been":[48],"accuracy":[50],"not":[52],"efficiency":[54],"process.":[57],"Another":[58],"queries":[62],"about":[63,88],"importance":[64],"features":[66],"one":[67],"at":[68],"time,":[70],"assuming":[71],"them":[72],"to":[73,92,105],"be":[74],"independent":[75],"hence":[77],"missing":[78],"covariance":[79,134],"information.":[80],"In":[81],"contrast,":[82],"we":[83,131],"propose":[84],"expert":[86],"pairwise":[89],"feature":[90],"similarities,":[91],"borrow":[93],"strength":[95],"predictions,":[98],"using":[100],"sequential":[101,142],"decision":[102],"making":[103],"techniques":[104],"minimize":[106],"effort":[108],"expert.":[111],"Empirical":[112],"results":[113],"demonstrate":[114],"improvement":[115],"performance":[118],"both":[120],"simulated":[121],"real":[123],"data,":[124],"linear":[127],"regression":[128],"tasks,":[129],"where":[130],"learn":[132],"structure":[135],"with":[136],"Gaussian":[138],"process,":[139],"based":[140],"elicitation.":[143]},"counts_by_year":[],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}