{"id":"https://openalex.org/W7138105419","doi":"https://doi.org/10.48550/arxiv.2603.14407","title":"Towards One-for-All Anomaly Detection for Tabular Data","display_name":"Towards One-for-All Anomaly Detection for Tabular Data","publication_year":2026,"publication_date":"2026-03-15","ids":{"openalex":"https://openalex.org/W7138105419","doi":"https://doi.org/10.48550/arxiv.2603.14407"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.14407","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.14407","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":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.14407","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129657884","display_name":"Shiyuan Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Li, Shiyuan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129680541","display_name":"Yixin Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Yixin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129680597","display_name":"Yu Zheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zheng, Yu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129644790","display_name":"Xiaofeng Cao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cao, Xiaofeng","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129669197","display_name":"Shirui Pan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pan, Shirui","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129728279","display_name":"Heng Tao Shen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shen, Heng Tao","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5129657884"],"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9883000254631042,"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":0.9883000254631042,"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":0.0017999999690800905,"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":0.0013000000035390258,"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/anomaly-detection","display_name":"Anomaly detection","score":0.8029000163078308},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.7311000227928162},{"id":"https://openalex.org/keywords/generalizability-theory","display_name":"Generalizability theory","score":0.6651999950408936},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.6445000171661377},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.636900007724762},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.42829999327659607}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.8029000163078308},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.7311000227928162},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.6651999950408936},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6516000032424927},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6445000171661377},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.636900007724762},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5828999876976013},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5299000144004822},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42829999327659607},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.41600000858306885},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41440001130104065},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.4065000116825104},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.3970000147819519},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.37869998812675476},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.2964000105857849},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.2554999887943268}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.14407","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.14407","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":"doi:10.48550/arxiv.2603.14407","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.14407","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":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Tabular":[0],"anomaly":[1,89,131,140,163],"detection":[2,164],"(TAD)":[3],"aims":[4],"to":[5,50,78,108,143],"identify":[6],"samples":[7],"that":[8,66,159],"deviate":[9],"from":[10,81,103,155],"the":[11,110,147,171],"majority":[12],"in":[13,19],"tabular":[14,88],"data":[15],"and":[16,40,46,75,98,133,166],"is":[17,127],"critical":[18],"many":[20],"real-world":[21],"applications.":[22],"However,":[23],"existing":[24],"methods":[25],"follow":[26],"a":[27,60,122,138],"``one":[28],"model":[29],"for":[30,129],"one":[31],"dataset":[32],"(OFO)''":[33],"paradigm,":[34],"which":[35],"relies":[36],"on":[37,71,152],"dataset-specific":[38],"training":[39,70,145],"thus":[41],"incurs":[42],"high":[43],"computational":[44],"cost":[45],"yields":[47],"limited":[48],"generalization":[49],"unseen":[51,79],"domains.":[52],"To":[53,85,116],"address":[54],"these":[55],"limitations,":[56],"we":[57],"propose":[58],"OFA-TAD,":[59],"generalist":[61],"one-for-all":[62,87],"(OFA)":[63],"TAD":[64],"framework":[65],"only":[67],"requires":[68],"one-time":[69],"multiple":[72,104],"source":[73],"datasets":[74,80,154],"can":[76],"generalize":[77],"diverse":[82],"domains":[83,157],"on-the-fly.":[84],"realize":[86],"detection,":[90],"OFA-TAD":[91,160],"extracts":[92],"neighbor-distance":[93,101],"patterns":[94],"as":[95],"transferable":[96],"cues,":[97],"introduces":[99],"multi-view":[100,119],"representations":[102],"transformation-induced":[105],"metric":[106],"spaces":[107],"mitigate":[109],"transformation":[111],"sensitivity":[112],"of":[113],"distance":[114,120],"profiles.":[115],"adaptively":[117],"combine":[118],"evidence,":[121],"Mixture-of-Experts":[123],"(MoE)":[124],"scoring":[125,132],"network":[126],"employed":[128],"view-specific":[130],"entropy-regularized":[134],"gated":[135],"fusion,":[136],"with":[137],"multi-strategy":[139],"synthesis":[141],"mechanism":[142],"support":[144],"under":[146,170],"one-class":[148],"constraint.":[149],"Extensive":[150],"experiments":[151],"34":[153],"14":[156],"demonstrate":[158],"achieves":[161],"superior":[162],"performance":[165],"strong":[167],"cross-domain":[168],"generalizability":[169],"strict":[172],"OFA":[173],"setting.":[174]},"counts_by_year":[],"updated_date":"2026-03-18T06:31:55.123368","created_date":"2026-03-18T00:00:00"}