{"id":"https://openalex.org/W7129553294","doi":"https://doi.org/10.48550/arxiv.2602.14622","title":"Tabular Foundation Models Can Learn Association Rules","display_name":"Tabular Foundation Models Can Learn Association Rules","publication_year":2026,"publication_date":"2026-02-16","ids":{"openalex":"https://openalex.org/W7129553294","doi":"https://doi.org/10.48550/arxiv.2602.14622"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.14622","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5052396143","display_name":"Erkan Karabulut","orcid":"https://orcid.org/0000-0003-2710-7951"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Karabulut, Erkan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126255935","display_name":"Daniel Daza","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Daza, Daniel","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126178026","display_name":"Paul Groth","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Groth, Paul","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004648901","display_name":"Martijn C. Schut","orcid":"https://orcid.org/0000-0003-4591-9646"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Schut, Martijn C.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5126251090","display_name":"Victoria Degeler","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Degeler, Victoria","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5052396143"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.8788999915122986,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10538","display_name":"Data Mining Algorithms and Applications","score":0.8788999915122986,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.014100000262260437,"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/T11063","display_name":"Rough Sets and Fuzzy Logic","score":0.012000000104308128,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/association-rule-learning","display_name":"Association rule learning","score":0.6958000063896179},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6535999774932861},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.6424000263214111},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4214000105857849},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.4205000102519989},{"id":"https://openalex.org/keywords/conditional-probability","display_name":"Conditional probability","score":0.42010000348091125},{"id":"https://openalex.org/keywords/association","display_name":"Association (psychology)","score":0.35339999198913574}],"concepts":[{"id":"https://openalex.org/C193524817","wikidata":"https://www.wikidata.org/wiki/Q386780","display_name":"Association rule learning","level":2,"score":0.6958000063896179},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6917999982833862},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6535999774932861},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.6424000263214111},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5896000266075134},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5702000260353088},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5303999781608582},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4214000105857849},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.4205000102519989},{"id":"https://openalex.org/C44492722","wikidata":"https://www.wikidata.org/wiki/Q327069","display_name":"Conditional probability","level":2,"score":0.42010000348091125},{"id":"https://openalex.org/C142853389","wikidata":"https://www.wikidata.org/wiki/Q744778","display_name":"Association (psychology)","level":2,"score":0.35339999198913574},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.3384000062942505},{"id":"https://openalex.org/C120567893","wikidata":"https://www.wikidata.org/wiki/Q1582085","display_name":"Knowledge extraction","level":2,"score":0.3359000086784363},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3154999911785126},{"id":"https://openalex.org/C12426560","wikidata":"https://www.wikidata.org/wiki/Q189569","display_name":"Basis (linear algebra)","level":2,"score":0.2994999885559082},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.2881999909877777},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.25870001316070557},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.2513999938964844}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.14622","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.14622","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.14622","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.14622","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Association":[0],"Rule":[1],"Mining":[2],"(ARM)":[3],"is":[4,15,168],"a":[5,65,73],"fundamental":[6],"task":[7],"for":[8,67],"knowledge":[9],"discovery":[10],"in":[11,18,48,160],"tabular":[12,58,89,126],"data":[13,59],"and":[14,33,138,157],"widely":[16],"used":[17],"high-stakes":[19],"decision-making.":[20],"Classical":[21],"ARM":[22,134],"methods":[23],"rely":[24],"on":[25,56,125,132],"frequent":[26,118],"itemset":[27,119],"mining,":[28],"leading":[29],"to":[30,93,112],"rule":[31,76,135],"explosion":[32],"poor":[34],"scalability,":[35],"while":[36],"recent":[37],"neural":[38],"approaches":[39],"mitigate":[40],"these":[41,69],"issues":[42],"but":[43],"suffer":[44],"from":[45,83],"degraded":[46],"performance":[47,156],"low-data":[49,161],"regimes.":[50],"Tabular":[51],"foundation":[52],"models":[53],"(TFMs),":[54],"pretrained":[55],"diverse":[57],"with":[60,153],"strong":[61,154],"in-context":[62],"generalization,":[63],"provide":[64],"basis":[66],"addressing":[68],"limitations.":[70],"We":[71,96,121],"introduce":[72,98],"model-agnostic":[74],"association":[75,81,114,151],"learning":[77],"framework":[78,104],"that":[79,105,145],"extracts":[80],"rules":[82,115,152],"any":[84],"conditional":[85,109],"probabilistic":[86],"model":[87],"over":[88],"data,":[90],"enabling":[91],"us":[92],"leverage":[94],"TFMs.":[95],"then":[97],"TabProbe,":[99],"an":[100],"instantiation":[101],"of":[102,128],"our":[103,123],"utilizes":[106],"TFMs":[107,146],"as":[108],"probability":[110],"estimators":[111],"learn":[113],"out-of-the-box":[116],"without":[117,163],"mining.":[120],"evaluate":[122],"approach":[124],"datasets":[127],"varying":[129],"sizes":[130],"based":[131],"standard":[133],"quality":[136],"metrics":[137],"downstream":[139],"classification":[140],"performance.":[141],"The":[142],"results":[143],"show":[144],"consistently":[147],"produce":[148],"concise,":[149],"high-quality":[150],"predictive":[155],"remain":[158],"robust":[159],"settings":[162],"task-specific":[164],"training.":[165],"Source":[166],"code":[167],"available":[169],"at":[170],"https://github.com/DiTEC-project/tabprobe.":[171]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2026-02-18T00:00:00"}