Abstract:We study the robustness of conformal prediction-a powerful tool for uncertainty quantification-to label noise. Our analysis tackles both regression and classification problems, characterizing when and how it is possible to construct uncertainty sets that correctly cover the unobserved noiseless ground truth labels. Through stylized theoretical examples and practical experiments, we argue that naïve conformal prediction covers the noiseless ground truth label unless the noise distribution is adversarially desig… Show more
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