{"id":"https://openalex.org/W4385568422","doi":"https://doi.org/10.1145/3580305.3599294","title":"Data-Efficient and Interpretable Tabular Anomaly Detection","display_name":"Data-Efficient and Interpretable Tabular Anomaly Detection","publication_year":2023,"publication_date":"2023-08-04","ids":{"openalex":"https://openalex.org/W4385568422","doi":"https://doi.org/10.1145/3580305.3599294"},"language":"en","primary_location":{"id":"doi:10.1145/3580305.3599294","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3580305.3599294","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","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/A5103218438","display_name":"Chun-Hao Chang","orcid":"https://orcid.org/0009-0009-7500-8019"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Chun-Hao Chang","raw_affiliation_strings":["Meta, Seattle, WA, USA"],"affiliations":[{"raw_affiliation_string":"Meta, Seattle, WA, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002289527","display_name":"Jinsung Yoon","orcid":"https://orcid.org/0000-0002-5481-5171"},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jinsung Yoon","raw_affiliation_strings":["Google Cloud AI, Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"Google Cloud AI, Mountain View, CA, USA","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111019833","display_name":"Sercan \u00d6. Arik","orcid":null},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sercan \u00d6. Arik","raw_affiliation_strings":["Google Cloud AI, Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"Google Cloud AI, Mountain View, CA, USA","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084564811","display_name":"Madeleine Udell","orcid":"https://orcid.org/0000-0002-3985-915X"},"institutions":[{"id":"https://openalex.org/I1743320","display_name":"Palo Alto University","ror":"https://ror.org/04f812k67","country_code":"US","type":"education","lineage":["https://openalex.org/I1743320"]},{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Madeleine Udell","raw_affiliation_strings":["Stanford University, Palo Alto, CA, USA"],"affiliations":[{"raw_affiliation_string":"Stanford University, Palo Alto, CA, USA","institution_ids":["https://openalex.org/I1743320","https://openalex.org/I97018004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5092595935","display_name":"Tomas Pfister","orcid":"https://orcid.org/0009-0004-4088-8718"},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tomas Pfister","raw_affiliation_strings":["Google Cloud AI, Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"Google Cloud AI, Mountain View, CA, USA","institution_ids":["https://openalex.org/I1291425158"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5103218438"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.7397,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.87574598,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"190","last_page":"201"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9998999834060669,"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":0.9998999834060669,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9901999831199646,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9886000156402588,"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/diad","display_name":"Diad","score":0.9248510003089905},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.6717134714126587},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6623817682266235},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6465743780136108},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5626642107963562},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5597831606864929},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5315090417861938},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.46790987253189087},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.45222240686416626},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.41207122802734375},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.38683992624282837}],"concepts":[{"id":"https://openalex.org/C2778512808","wikidata":"https://www.wikidata.org/wiki/Q16943522","display_name":"Diad","level":4,"score":0.9248510003089905},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.6717134714126587},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6623817682266235},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6465743780136108},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5626642107963562},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5597831606864929},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5315090417861938},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.46790987253189087},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45222240686416626},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.41207122802734375},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38683992624282837},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C15920480","wikidata":"https://www.wikidata.org/wiki/Q421281","display_name":"Copolymer","level":3,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C521977710","wikidata":"https://www.wikidata.org/wiki/Q81163","display_name":"Polymer","level":2,"score":0.0},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3580305.3599294","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3580305.3599294","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1996796871","https://openalex.org/W2282821441","https://openalex.org/W2560674852","https://openalex.org/W2618851150","https://openalex.org/W2804236575","https://openalex.org/W2950858167","https://openalex.org/W2962862931","https://openalex.org/W2990875097","https://openalex.org/W2993995076","https://openalex.org/W3128465814","https://openalex.org/W3166166117","https://openalex.org/W3171322015","https://openalex.org/W3176678956","https://openalex.org/W3195225944","https://openalex.org/W4212774754","https://openalex.org/W4254182148","https://openalex.org/W4289097366","https://openalex.org/W4376626035","https://openalex.org/W6600773522","https://openalex.org/W6600955100"],"related_works":["https://openalex.org/W2325591455","https://openalex.org/W2093549014","https://openalex.org/W1546488784","https://openalex.org/W2595172197","https://openalex.org/W2474930049","https://openalex.org/W4307269956","https://openalex.org/W2951103099","https://openalex.org/W3145772462","https://openalex.org/W2084856301","https://openalex.org/W2127970246"],"abstract_inverted_index":{"Anomaly":[0],"detection":[1],"(AD)":[2],"plays":[3],"an":[4],"important":[5],"role":[6],"in":[7,41,43,121,134],"numerous":[8],"applications.":[9,28],"In":[10,73],"this":[11,74],"paper,":[12,75],"we":[13,76],"focus":[14],"on":[15,140,150],"two":[16],"understudied":[17],"aspects":[18],"of":[19,113,128,148],"AD":[20,31,56,80,119],"that":[21,37,63,83],"are":[22,38,58],"critical":[23],"for":[24],"integration":[25],"into":[26],"real-world":[27],"First,":[29],"most":[30,55],"methods":[32,57],"cannot":[33],"incorporate":[34,109],"labeled":[35,114],"data":[36,115],"often":[39],"available":[40],"practice":[42],"small":[44,111],"quantities":[45],"and":[46,137],"can":[47,108],"be":[48],"crucial":[49],"to":[50,92,116,131],"achieve":[51],"high":[52],"accuracy.":[53],"Second,":[54],"not":[59],"interpretable,":[60],"a":[61,78,85,96,110],"bottleneck":[62],"prevents":[64],"stakeholders":[65],"from":[66],"understanding":[67],"the":[68,71,126],"reason":[69],"behind":[70,153],"anomalies.":[72,158],"propose":[77],"novel":[79],"framework,":[81],"DIAD,":[82,149],"adapts":[84],"white-box":[86],"model":[87],"class,":[88],"Generalized":[89],"Additive":[90],"Models,":[91],"detect":[93],"anomalies":[94],"using":[95],"partial":[97],"identification":[98],"objective":[99],"which":[100],"naturally":[101],"handles":[102],"noisy":[103],"or":[104],"heterogeneous":[105],"features.":[106],"DIAD":[107,129],"amount":[112],"further":[117],"boost":[118],"performances":[120],"semi-supervised":[122,138],"settings.":[123],"We":[124,143],"demonstrate":[125],"superiority":[127],"compared":[130],"previous":[132],"work":[133],"both":[135],"unsupervised":[136],"settings":[139],"multiple":[141],"datasets.":[142],"also":[144],"present":[145],"explainability":[146],"capabilities":[147],"its":[151],"rationale":[152],"predicting":[154],"certain":[155],"samples":[156],"as":[157]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}