{"id":"https://openalex.org/W7131278152","doi":"https://doi.org/10.1007/s10994-025-06984-x","title":"Automated Machine Learning for Unsupervised Tabular Tasks","display_name":"Automated Machine Learning for Unsupervised Tabular Tasks","publication_year":2026,"publication_date":"2026-02-24","ids":{"openalex":"https://openalex.org/W7131278152","doi":"https://doi.org/10.1007/s10994-025-06984-x"},"language":"en","primary_location":{"id":"doi:10.1007/s10994-025-06984-x","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-025-06984-x","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-025-06984-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/s10994-025-06984-x.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103908992","display_name":"P. M. Singh","orcid":null},"institutions":[{"id":"https://openalex.org/I83019370","display_name":"Eindhoven University of Technology","ror":"https://ror.org/02c2kyt77","country_code":"NL","type":"education","lineage":["https://openalex.org/I83019370"]}],"countries":["NL"],"is_corresponding":true,"raw_author_name":"Prabhant Singh","raw_affiliation_strings":["AMOR/e Lab, Eindhoven University of Technology, 5600 MB, Eindhoven, The Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"AMOR/e Lab, Eindhoven University of Technology, 5600 MB, Eindhoven, The Netherlands","institution_ids":["https://openalex.org/I83019370"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081552858","display_name":"Pieter Gijsbers","orcid":"https://orcid.org/0000-0001-7346-8075"},"institutions":[{"id":"https://openalex.org/I83019370","display_name":"Eindhoven University of Technology","ror":"https://ror.org/02c2kyt77","country_code":"NL","type":"education","lineage":["https://openalex.org/I83019370"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Pieter Gijsbers","raw_affiliation_strings":["AMOR/e Lab, Eindhoven University of Technology, 5600 MB, Eindhoven, The Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"AMOR/e Lab, Eindhoven University of Technology, 5600 MB, Eindhoven, The Netherlands","institution_ids":["https://openalex.org/I83019370"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001094091","display_name":"Elif Ceren Gok Yildirim","orcid":null},"institutions":[{"id":"https://openalex.org/I83019370","display_name":"Eindhoven University of Technology","ror":"https://ror.org/02c2kyt77","country_code":"NL","type":"education","lineage":["https://openalex.org/I83019370"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Elif Ceren Gok Yildirim","raw_affiliation_strings":["AMOR/e Lab, Eindhoven University of Technology, 5600 MB, Eindhoven, The Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"AMOR/e Lab, Eindhoven University of Technology, 5600 MB, Eindhoven, The Netherlands","institution_ids":["https://openalex.org/I83019370"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061070845","display_name":"Murat Onur Yildirim","orcid":"https://orcid.org/0000-0002-8203-0179"},"institutions":[{"id":"https://openalex.org/I83019370","display_name":"Eindhoven University of Technology","ror":"https://ror.org/02c2kyt77","country_code":"NL","type":"education","lineage":["https://openalex.org/I83019370"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Murat Onur Yildirim","raw_affiliation_strings":["AMOR/e Lab, Eindhoven University of Technology, 5600 MB, Eindhoven, The Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"AMOR/e Lab, Eindhoven University of Technology, 5600 MB, Eindhoven, The Netherlands","institution_ids":["https://openalex.org/I83019370"]}]},{"author_position":"last","author":{"id":null,"display_name":"Joaquin Vanschoren","orcid":null},"institutions":[{"id":"https://openalex.org/I83019370","display_name":"Eindhoven University of Technology","ror":"https://ror.org/02c2kyt77","country_code":"NL","type":"education","lineage":["https://openalex.org/I83019370"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Joaquin Vanschoren","raw_affiliation_strings":["AMOR/e Lab, Eindhoven University of Technology, 5600 MB, Eindhoven, The Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"AMOR/e Lab, Eindhoven University of Technology, 5600 MB, Eindhoven, The Netherlands","institution_ids":["https://openalex.org/I83019370"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5103908992"],"corresponding_institution_ids":["https://openalex.org/I83019370"],"apc_list":{"value":2990,"currency":"USD","value_usd":2990},"apc_paid":{"value":2990,"currency":"USD","value_usd":2990},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.21935536,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"115","issue":"3","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.6176999807357788,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.6176999807357788,"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":0.15139999985694885,"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":0.043800000101327896,"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/unsupervised-learning","display_name":"Unsupervised learning","score":0.7502999901771545},{"id":"https://openalex.org/keywords/intuition","display_name":"Intuition","score":0.5479000210762024},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.541100025177002},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.5005000233650208},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.4634000062942505},{"id":"https://openalex.org/keywords/model-selection","display_name":"Model selection","score":0.43320000171661377},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.39980000257492065},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.39969998598098755},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.35989999771118164}],"concepts":[{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7903000116348267},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7897999882698059},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.77920001745224},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.7502999901771545},{"id":"https://openalex.org/C132010649","wikidata":"https://www.wikidata.org/wiki/Q189222","display_name":"Intuition","level":2,"score":0.5479000210762024},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.541100025177002},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.5005000233650208},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.4634000062942505},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.43320000171661377},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.39980000257492065},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.39969998598098755},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.35989999771118164},{"id":"https://openalex.org/C115903097","wikidata":"https://www.wikidata.org/wiki/Q7094097","display_name":"Online machine learning","level":3,"score":0.35519999265670776},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.33219999074935913},{"id":"https://openalex.org/C175309249","wikidata":"https://www.wikidata.org/wiki/Q725864","display_name":"Pipeline transport","level":2,"score":0.32260000705718994},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.31150001287460327},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.28870001435279846},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.2856000065803528},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.273499995470047},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.26759999990463257},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2637999951839447},{"id":"https://openalex.org/C2779714256","wikidata":"https://www.wikidata.org/wiki/Q25305062","display_name":"Multiple Models","level":2,"score":0.2628999948501587},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.2615000009536743},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2554999887943268},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.25369998812675476}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s10994-025-06984-x","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-025-06984-x","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-025-06984-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"}],"best_oa_location":{"id":"doi:10.1007/s10994-025-06984-x","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-025-06984-x","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-025-06984-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":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7131278152.pdf","grobid_xml":"https://content.openalex.org/works/W7131278152.grobid-xml"},"referenced_works_count":28,"referenced_works":["https://openalex.org/W1242748811","https://openalex.org/W1507030697","https://openalex.org/W1530232915","https://openalex.org/W1584412742","https://openalex.org/W1986332411","https://openalex.org/W1992276041","https://openalex.org/W2027842533","https://openalex.org/W2085487226","https://openalex.org/W2123649031","https://openalex.org/W2132862423","https://openalex.org/W2142838865","https://openalex.org/W2144182447","https://openalex.org/W2150593711","https://openalex.org/W2160642098","https://openalex.org/W2165232124","https://openalex.org/W2291624323","https://openalex.org/W2296719434","https://openalex.org/W3023012876","https://openalex.org/W3128386428","https://openalex.org/W3134987911","https://openalex.org/W3163570135","https://openalex.org/W4211208325","https://openalex.org/W4213308398","https://openalex.org/W4233762729","https://openalex.org/W4297825866","https://openalex.org/W4312433903","https://openalex.org/W4381329178","https://openalex.org/W4385763910"],"related_works":[],"abstract_inverted_index":{"Abstract":[0],"In":[1],"this":[2,41,76],"work,":[3],"we":[4],"present":[5,102],"Learning":[6],"to":[7,21,74],"Learn":[8],"with":[9,63,87,108],"Optimal":[10,71],"Transport":[11,72],"for":[12,25,126],"Unsupervised":[13],"Scenarios":[14],"(LOTUS),":[15],"a":[16,45,53,64,118],"simple":[17],"yet":[18],"effective":[19],"method":[20,91],"perform":[22,50],"model":[23,124],"selection":[24,125],"multiple":[26,127],"unsupervised":[27,95,128],"machine":[28,46,84],"learning":[29,47,85],"(ML)":[30],"tasks":[31],"such":[32],"as":[33],"outlier":[34,97],"detection":[35,98],"and":[36,82,99,113],"clustering.":[37,100],"Our":[38],"intuition":[39],"behind":[40],"work":[42],"is":[43,117],"that":[44,115],"pipeline":[48],"will":[49],"well":[51,60],"in":[52],"new":[54],"dataset":[55],"if":[56],"it":[57],"previously":[58],"worked":[59],"on":[61,92],"datasets":[62,81],"similar":[65],"underlying":[66],"data":[67],"distribution.":[68],"We":[69,101],"use":[70],"distances":[73],"find":[75],"similarity":[77],"between":[78],"unlabeled":[79],"tabular":[80],"recommend":[83],"pipelines":[86],"one":[88],"unified":[89],"single":[90],"two":[93],"downstream":[94],"tasks:":[96],"the":[103],"effectiveness":[104],"of":[105],"our":[106],"approach":[107],"experiments":[109],"against":[110],"strong":[111],"baselines":[112],"show":[114],"LOTUS":[116],"very":[119],"promising":[120],"first":[121],"step":[122],"toward":[123],"ML":[129],"tasks.":[130]},"counts_by_year":[],"updated_date":"2026-07-23T08:03:31.855105","created_date":"2026-02-25T00:00:00"}
