{"id":"https://openalex.org/W7131392643","doi":"https://doi.org/10.48550/arxiv.2602.20153","title":"JUCAL: Jointly Calibrating Aleatoric and Epistemic Uncertainty in Classification Tasks","display_name":"JUCAL: Jointly Calibrating Aleatoric and Epistemic Uncertainty in Classification Tasks","publication_year":2026,"publication_date":"2026-02-23","ids":{"openalex":"https://openalex.org/W7131392643","doi":"https://doi.org/10.48550/arxiv.2602.20153"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2602.20153","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.20153","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":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.2602.20153","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5010858034","display_name":"Jakob Heiss","orcid":"https://orcid.org/0000-0003-1447-6782"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Heiss, Jakob","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086879387","display_name":"S\u00f6ren Lambrecht","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lambrecht, S\u00f6ren","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089458452","display_name":"Jakob Weissteiner","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Weissteiner, Jakob","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036552778","display_name":"Hanna Wutte","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wutte, Hanna","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001722531","display_name":"\u017dan \u017duri\u010d","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"\u017duri\u010d, \u017dan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084818994","display_name":"Josef Teichmann","orcid":"https://orcid.org/0000-0003-0125-7484"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Teichmann, Josef","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5126809103","display_name":"Bin Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Bin","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5010858034"],"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.2547999918460846,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.2547999918460846,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.23960000276565552,"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":0.09849999845027924,"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/calibration","display_name":"Calibration","score":0.7706999778747559},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6596999764442444},{"id":"https://openalex.org/keywords/uncertainty-quantification","display_name":"Uncertainty quantification","score":0.6499000191688538},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.579200029373169},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.5684999823570251},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.41370001435279846},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.3824999928474426},{"id":"https://openalex.org/keywords/scoring-rule","display_name":"Scoring rule","score":0.3084999918937683}],"concepts":[{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.7706999778747559},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6596999764442444},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.6499000191688538},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5952000021934509},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.579200029373169},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.5684999823570251},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5598999857902527},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5442000031471252},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.41370001435279846},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.3824999928474426},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34369999170303345},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3158000111579895},{"id":"https://openalex.org/C63002673","wikidata":"https://www.wikidata.org/wiki/Q2260590","display_name":"Scoring rule","level":2,"score":0.3084999918937683},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.30630001425743103},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.3043000102043152},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.30140000581741333},{"id":"https://openalex.org/C137209882","wikidata":"https://www.wikidata.org/wiki/Q1403517","display_name":"Measurement uncertainty","level":2,"score":0.2985000014305115},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.2879999876022339},{"id":"https://openalex.org/C917703","wikidata":"https://www.wikidata.org/wiki/Q7239668","display_name":"Predictive inference","level":5,"score":0.2865999937057495},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.26910001039505005},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.26840001344680786},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.26579999923706055},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.25279998779296875}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2602.20153","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.20153","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:10.48550/arxiv.2602.20153","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.20153","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":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":{"We":[0,156],"study":[1],"post-calibration":[2],"uncertainty":[3],"for":[4,165,248],"trained":[5,134],"ensembles":[6,166,216,250],"of":[7,43,55,136,167,210,217,224],"classifiers.":[8],"Specifically,":[9],"we":[10,86,240],"consider":[11],"both":[12],"aleatoric":[13,56,101,116],"(label":[14],"noise)":[15],"and":[16,24,36,57,102,112,115,170,192,200,226],"epistemic":[17,58,103,114],"(model)":[18],"uncertainty.":[19,59,104],"Among":[20],"the":[21,40,53,120,125,152],"most":[22],"popular":[23],"widely":[25],"used":[26],"calibration":[27,45,92,183],"methods":[28,46,184],"in":[29,73,80,222,231,251],"classification":[30,163,188],"are":[31],"temperature":[32],"scaling":[33],"(i.e.,":[34],"pool-then-calibrate)":[35],"conformal":[37],"methods.":[38],"However,":[39],"main":[41],"shortcoming":[42],"these":[44,62],"is":[47],"that":[48,98,178],"they":[49],"do":[50],"not":[51],"balance":[52],"proportion":[54],"Not":[60],"balancing":[61],"uncertainties":[63,117],"can":[64,129,213],"severely":[65],"misrepresent":[66],"predictive":[67,193,227],"uncertainty,":[68],"leading":[69],"to":[70,110,132,151,198,207,220,233],"overconfident":[71],"predictions":[72],"some":[74],"input":[75],"regions":[76],"while":[77],"being":[78],"underconfident":[79],"others.":[81],"To":[82],"address":[83],"this":[84],"shortcoming,":[85],"present":[87],"a":[88,244],"simple":[89],"but":[90],"powerful":[91],"algorithm":[93],"Joint":[94],"Uncertainty":[95],"Calibration":[96],"(JUCAL)":[97],"jointly":[99,106],"calibrates":[100,107],"JUCAL":[105,128,159,179,206,242],"two":[108],"constants":[109],"weight":[111],"scale":[113],"by":[118,196],"optimizing":[119],"negative":[121],"log-likelihood":[122],"(NLL)":[123],"on":[124,160],"validation/calibration":[126],"dataset.":[127],"be":[130],"applied":[131],"any":[133],"ensemble":[135,209],"classifiers":[137],"(e.g.,":[138],"transformers,":[139],"CNNs,":[140],"or":[141],"tree-based":[142],"methods),":[143],"with":[144,171],"minimal":[145],"computational":[146],"overhead,":[147],"without":[148],"requiring":[149],"access":[150],"models'":[153],"internal":[154],"parameters.":[155],"experimentally":[157],"evaluate":[158],"various":[161],"text":[162],"tasks,":[164,189],"varying":[168],"sizes":[169],"different":[172],"ensembling":[173],"strategies.":[174],"Our":[175],"experiments":[176],"show":[177],"significantly":[180],"outperforms":[181],"SOTA":[182],"across":[185],"all":[186],"considered":[187],"reducing":[190],"NLL":[191,225],"set":[194,228],"size":[195,211,218],"up":[197,219,232],"15%":[199],"20%,":[201],"respectively.":[202],"Interestingly,":[203],"even":[204],"applying":[205],"an":[208],"5":[212],"outperform":[214],"temperature-scaled":[215],"50":[221],"terms":[223],"size,":[229],"resulting":[230],"10":[234],"times":[235],"smaller":[236],"inference":[237],"costs.":[238],"Thus,":[239],"propose":[241],"as":[243],"new":[245],"go-to":[246],"method":[247],"calibrating":[249],"classification.":[252]},"counts_by_year":[],"updated_date":"2026-02-26T06:34:08.959763","created_date":"2026-02-26T00:00:00"}