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This is obtained through a precise characterization of the coverage inflation (or deflation) suffered by standard conformal inferences in the presence of label contamination, which is then made actionable through a new calibration algorithm. Our solution can leverage different modelling assumptions about the contamination process, while requiring no knowledge of the underlying data distribution or of the inner workings of the classification model. The empirical performance of the proposed method is demonstrated through simulations and an application to object classification with the CIFAR-10H image data set.<\/jats:p>","DOI":"10.1093\/jrsssb\/qkae114","type":"journal-article","created":{"date-parts":[[2024,11,24]],"date-time":"2024-11-24T23:23:08Z","timestamp":1732490588000},"page":"796-815","source":"Crossref","is-referenced-by-count":3,"title":["Adaptive conformal classification with noisy labels"],"prefix":"10.1093","volume":"87","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9046-907X","authenticated-orcid":false,"given":"Matteo","family":"Sesia","sequence":"first","affiliation":[{"name":"Department of Data Sciences and Operations, Marshall School of Business, University of Southern California , Los Angeles, CA 90089 ,","place":["USA"]},{"name":"Thomas Lord Department of Computer Science, Viterbi School of Engineering, University of Southern California , Los Angeles, CA 90089 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