{"id":"https://openalex.org/W7160915169","doi":"https://doi.org/10.48550/arxiv.2605.09857","title":"Unified Approach for Weakly Supervised Multicalibration","display_name":"Unified Approach for Weakly Supervised Multicalibration","publication_year":2026,"publication_date":"2026-05-11","ids":{"openalex":"https://openalex.org/W7160915169","doi":"https://doi.org/10.48550/arxiv.2605.09857"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.09857","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.09857","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.2605.09857","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5024912504","display_name":"Futoshi Futami","orcid":"https://orcid.org/0000-0002-0661-0729"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Futami, Futoshi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135952174","display_name":"Takashi Ishida","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ishida, Takashi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":[],"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/T12535","display_name":"Machine Learning and Data Classification","score":0.24899999797344208,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.24899999797344208,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.09690000116825104,"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":0.08659999817609787,"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/estimator","display_name":"Estimator","score":0.7235999703407288},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.599399983882904},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.41179999709129333},{"id":"https://openalex.org/keywords/statistical-learning","display_name":"Statistical learning","score":0.3325999975204468},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.3325999975204468},{"id":"https://openalex.org/keywords/limit","display_name":"Limit (mathematics)","score":0.30799999833106995}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.7235999703407288},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.599399983882904},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5630999803543091},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.459199994802475},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45809999108314514},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.41179999709129333},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.3325999975204468},{"id":"https://openalex.org/C2982736386","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Statistical learning","level":2,"score":0.3325999975204468},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32600000500679016},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.32409998774528503},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.30799999833106995},{"id":"https://openalex.org/C45493050","wikidata":"https://www.wikidata.org/wiki/Q7884934","display_name":"Unified Model","level":2,"score":0.28630000352859497},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.28459998965263367},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.2825999855995178},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.2696000039577484},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.26919999718666077},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.26420000195503235},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.25619998574256897},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2549000084400177},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.25360000133514404}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.09857","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.09857","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.2605.09857","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.09857","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Multicalibration":[0],"requires":[1],"predicted":[2],"scores":[3],"to":[4,136],"agree":[5],"with":[6,102,110],"label":[7],"probabilities":[8],"across":[9,132],"rich":[10],"families":[11],"of":[12,67],"subgroups":[13],"and":[14,26,41,70,91,140],"score-dependent":[15],"tests,":[16],"but":[17],"existing":[18],"methods":[19,73],"require":[20],"clean":[21,46,79],"input-label":[22,80],"pairs":[23,81],"for":[24,74,89],"evaluation":[25],"post-processing.":[27],"This":[28],"assumption":[29],"fails":[30],"in":[31,77],"weakly":[32],"supervised":[33],"learning":[34,43],"(WSL)":[35],"regimes":[36],"--":[37,44],"including":[38],"positive-unlabeled,":[39],"unlabeled-unlabeled,":[40],"positive-confidence":[42],"where":[45],"labels":[47],"are":[48,82],"costly":[49],"or":[50],"unavailable":[51],"even":[52],"though":[53],"reliable":[54],"uncertainty":[55,145],"estimates":[56],"may":[57],"be":[58],"crucial.":[59],"We":[60,84,113],"address":[61],"this":[62],"gap":[63],"by":[64,97],"developing":[65],"estimators":[66],"multicalibration":[68,93,108,117,138],"error":[69],"post-hoc":[71,122],"correction":[72],"WSL":[75],"settings":[76,135],"which":[78],"unavailable.":[83],"propose":[85,115],"a":[86,120],"unified":[87],"framework":[88],"estimating":[90],"correcting":[92],"under":[94,125,147],"weak":[95,126,148],"supervision":[96],"combining":[98],"contamination-matrix":[99],"risk":[100],"rewrites":[101],"witness-based":[103],"calibration":[104],"constraints,":[105],"yielding":[106],"corrected":[107],"moments":[109],"finite-sample":[111],"guarantees.":[112],"further":[114],"weak-label":[116],"boost":[118],"(WLMC),":[119],"generic":[121],"recalibration":[123],"algorithm":[124],"supervision.":[127,149],"Finally,":[128],"we":[129],"conduct":[130],"experiments":[131],"multiple":[133],"weak-supervision":[134],"evaluate":[137],"behavior":[139],"offer":[141],"empirical":[142],"insight":[143],"into":[144],"estimation":[146]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-13T00:00:00"}