{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T03:05:27Z","timestamp":1773803127891,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"27","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>Conformal prediction (CP) is a general framework to quantify the predictive uncertainty of machine learning models that uses a set prediction to include the true label with a valid probability.\nTo align the uncertainty measured by CP, conformal training methods minimize the size of the prediction sets.\nA typical way is to use a surrogate indicator function, usually Sigmoid or Gaussian error function.\nHowever, these surrogate functions do not have a uniform error bound to the indicator function, leading to uncontrollable learning bounds.\nIn this paper, we propose a simple cost-sensitive conformal training algorithm that does not rely on the indicator approximation mechanism.\nSpecifically, we theoretically show that minimizing the expected size of prediction sets is upper bounded by the expected rank of true labels.\nTo this end, we develop an importance weighting strategy that assigns the weight using the rank of true label on each data.\nOur analysis provably demonstrates the tightness between the proposed weighted objective and the expected size of conformal prediction sets.\nExtensive experiments verify the validity of our theoretical insights, \nand superior empirical performance over other conformal training in terms of predictive efficiency with 21.38% reduction for average prediction set size.<\/jats:p>","DOI":"10.1609\/aaai.v40i27.39384","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T01:37:40Z","timestamp":1773797860000},"page":"22274-22282","source":"Crossref","is-referenced-by-count":0,"title":["Cost-Sensitive Conformal Training with Provably Controllable Learning Bounds"],"prefix":"10.1609","volume":"40","author":[{"given":"Xuesong","family":"Jia","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanjie","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziquan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"9382","published-online":{"date-parts":[[2026,3,14]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/39384\/43345","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/39384\/43345","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T01:37:40Z","timestamp":1773797860000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/39384"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":0,"journal-issue":{"issue":"27","published-online":{"date-parts":[[2026,3,17]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v40i27.39384","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,14]]}}}