{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T18:15:29Z","timestamp":1762539329971,"version":"build-2065373602"},"reference-count":44,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"12","license":[{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"European Union \u201cNextGenerationEU\u201d\/PRTR through","award":["MCIN\/AEI\/10.13039\/501100011033","PID2022-137063NBI00","CEX2021-001142-S","MCIN\/AEI\/10.13039\/501100011033"],"award-info":[{"award-number":["MCIN\/AEI\/10.13039\/501100011033","PID2022-137063NBI00","CEX2021-001142-S","MCIN\/AEI\/10.13039\/501100011033"]}]},{"name":"European Union \u201cNextGenerationEU\u201d\/PRTR"},{"DOI":"10.13039\/501100003086","name":"Eusko Jaurlaritza","doi-asserted-by":"publisher","award":["BERC-2022-2025"],"award-info":[{"award-number":["BERC-2022-2025"]}],"id":[{"id":"10.13039\/501100003086","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["IIS-2211386"],"award-info":[{"award-number":["IIS-2211386"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003086","name":"Eusko Jaurlaritza","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003086","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1109\/tpami.2025.3597508","type":"journal-article","created":{"date-parts":[[2025,8,11]],"date-time":"2025-08-11T17:43:12Z","timestamp":1754934192000},"page":"11534-11547","source":"Crossref","is-referenced-by-count":0,"title":["Reliable Programmatic Weak Supervision With Confidence Intervals for Label Probabilities"],"prefix":"10.1109","volume":"47","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9775-5888","authenticated-orcid":false,"given":"Ver\u00f3nica","family":"\u00c1lvarez","sequence":"first","affiliation":[{"name":"Massachusetts Institute of Technology (MIT), Cambridge, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6608-8581","authenticated-orcid":false,"given":"Santiago","family":"Mazuelas","sequence":"additional","affiliation":[{"name":"BCAM-Basque Center for Applied Mathematics, Bilbao, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-8226-2892","authenticated-orcid":false,"given":"Steven","family":"An","sequence":"additional","affiliation":[{"name":"University of California, San Diego, San Diego, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5960-5157","authenticated-orcid":false,"given":"Sanjoy","family":"Dasgupta","sequence":"additional","affiliation":[{"name":"University of California, San Diego, San Diego, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00041"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3094662"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3236459"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2024.3388209"},{"key":"ref5","article-title":"Learning hyper\n      label model for programmatic weak supervision","volume-title":"Proc. Int.\n      Conf. Learn. Representations","author":"Wu","year":"2022"},{"key":"ref6","first-page":"3574","article-title":"Data\n      programming: Creating large training sets, quickly","volume-title":"Proc.\n      Adv. Neural Inf. Process. Syst.","author":"Ratner","year":"2016"},{"key":"ref7","first-page":"269","article-title":"Snorkel:\n      Rapid training data creation with weak supervision","volume-title":"Proc. VLDB\n      Endowment","volume":"11","author":"Ratner","year":"2017"},{"key":"ref8","article-title":"Universalizing\n      weak supervision","volume-title":"Proc. Int. Conf. Learn.\n      Representations","author":"Shin","year":"2021"},{"key":"ref9","first-page":"3238","article-title":"Fast and\n      three-rious: Speeding up weak supervision with triplet methods","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Fu","year":"2020"},{"key":"ref10","first-page":"7534","article-title":"Adversarial\n      multi class learning under weak supervision with performance\n     guarantees","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Mazzetto","year":"2021"},{"key":"ref11","first-page":"37563","article-title":"Lifting weak\n      supervision to structured prediction","volume-title":"Proc. Adv. Neural\n      Inf. Process. Syst.","author":"Vishwakarma","year":"2022"},{"key":"ref12","first-page":"8214","article-title":"Firebolt: Weak supervision under weaker\n      assumptions","volume-title":"Proc. Int. Conf. Artif. Intell.\n      Statist.","author":"Kuang","year":"2022"},{"key":"ref13","first-page":"34394","article-title":"Robust weak\n      supervision with variational auto-encoders","volume-title":"Proc. Int.\n      Conf. Mach. Learn.","author":"Tonolini","year":"2023"},{"key":"ref14","first-page":"157","article-title":"Leveraging\n      instance features for label aggregation in programmatic weak\n     supervision","volume-title":"Proc. Int. Conf. Artif. Intell.\n      Statist.","author":"Zhang","year":"2023"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.2307\/2346806"},{"key":"ref16","first-page":"3886","article-title":"Exploiting\n      worker correlation for label aggregation in crowdsourcing","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Li","year":"2019"},{"key":"ref17","first-page":"6418","article-title":"Learning\n      dependency structures for weak supervision models","volume-title":"Proc.\n      Int. Conf. Mach. Learn.","author":"Varma","year":"2019"},{"key":"ref18","first-page":"273","article-title":"Learning\n      the structure of generative models without labeled data","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Bach","year":"2017"},{"issue":"1","key":"ref19","first-page":"5254","article-title":"A general\n      framework for adversarial label learning","volume":"22","author":"Arachie","year":"2021","journal-title":"J. Mach. Learn.\n      Res."},{"key":"ref20","first-page":"5039","article-title":"Optimal binary\n      classifier aggregation for general losses","volume-title":"Proc. Adv.\n      Neural Inf. Process. Syst.","author":"Balsubramani","year":"2016"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.14778\/2809974.2809991"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.3115\/1690219.1690287"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/3318464.3380592"},{"article-title":"A survey on\n      programmatic weak supervision","year":"2022","author":"Zhang","key":"ref24"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1214\/20-EJS1749"},{"key":"ref26","first-page":"3711","article-title":"Distribution-free binary classification: Prediction sets, confidence\n      intervals and calibration","volume-title":"Proc. Adv. Neural Inf. Process.\n      Syst.","author":"Gupta","year":"2020"},{"article-title":"Error rate bounds\n      and iterative weighted majority voting for crowdsourcing","year":"2014","author":"Li","key":"ref27"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.21236\/ADA459827"},{"key":"ref29","first-page":"559","article-title":"Adversarial\n      multiclass classification: A risk minimization perspective","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Fathony","year":"2016"},{"key":"ref30","first-page":"302","article-title":"Minimax\n      classification with 0-1 loss and performance guarantees","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Mazuelas","year":"2020"},{"key":"ref31","first-page":"65678","article-title":"Minimax\n      forward and backward learning of evolving tasks with performance\n     guarantees","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"\u00c1lvarez","year":"2023"},{"key":"ref32","first-page":"391","article-title":"Structural\n      maxent models","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Cortes","year":"2015"},{"issue":"68","key":"ref33","first-page":"1","article-title":"Variance-based regularization with convex\n     objectives","volume":"20","author":"Duchi","year":"2019","journal-title":"J. Mach. Learn. Res."},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2022.3143764"},{"key":"ref35","article-title":"Convergence\n      behavior of an adversarial weak supervision method","volume-title":"Proc.\n      Conf. Uncertainty Artif. Intell.","author":"An","year":"2024"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4613-3557-3_1"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2857768"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.findings-emnlp.334"},{"key":"ref39","article-title":"WRENCH: A comprehensive benchmark for weak\n      supervision","volume-title":"Proc. Conf. Neural Inf. Process. Syst.\n      Datasets Benchmarks Track","author":"Zhang","year":"2021"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298698"},{"key":"ref41","article-title":"Learning\n      from rules generalizing labeled exemplars","volume-title":"Proc. Int. Conf.\n      Learn. Representations","author":"Awasthi","year":"2020"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33013183"},{"key":"ref43","first-page":"3196","article-title":"Semi-supervised\n      aggregation of dependent weak supervision sources with performance\n     guarantees","volume-title":"Proc. Int. Conf. Artif. Intell.\n      Statist.","author":"Mazzetto","year":"2021"},{"key":"ref44","first-page":"3792","article-title":"Verified\n      uncertainty calibration","volume-title":"Proc. Adv. Neural Inf. Process.\n      Syst.","author":"Kumar","year":"2019"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ieeexplore.ieee.org\/ielam\/34\/11230086\/11122424-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/34\/11230086\/11122424.pdf?arnumber=11122424","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T18:10:06Z","timestamp":1762539006000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11122424\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12]]},"references-count":44,"journal-issue":{"issue":"12"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2025.3597508","relation":{},"ISSN":["0162-8828","2160-9292","1939-3539"],"issn-type":[{"type":"print","value":"0162-8828"},{"type":"electronic","value":"2160-9292"},{"type":"electronic","value":"1939-3539"}],"subject":[],"published":{"date-parts":[[2025,12]]}}}