  This paper develops novel conformal prediction methods for classification
tasks that can automatically adapt to random label contamination in the
calibration sample, leading to more informative prediction sets with stronger
coverage guarantees compared to state-of-the-art approaches. This is made
possible by a precise characterization of the effective coverage inflation (or
deflation) suffered by standard conformal inferences in the presence of label
contamination, which is then made actionable through new calibration
algorithms. Our solution is flexible and can leverage different modeling
assumptions about the label contamination process, while requiring no knowledge
of the underlying data distribution or of the inner workings of the
machine-learning classifier. The advantages of the proposed methods are
demonstrated through extensive simulations and an application to object
classification with the CIFAR-10H image data set.
