Adaptive conformal classification with noisy labels

arXiv:2309.05092v2 Announce Type: replace-cross
Abstract: 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 …

abstract adapt art arxiv classification coverage cs.lg inflation labels math.st novel paper prediction random sample state stat.me stat.th tasks type