Label Noise Robustness of Conformal Prediction
This addresses uncertainty quantification reliability for practitioners dealing with noisy datasets, though it is incremental in extending existing conformal prediction theory.
The paper investigates the robustness of conformal prediction to label noise in regression and classification, showing that it conservatively covers clean labels under dispersive noise and can be corrected for bounded noise to achieve correct risk without requiring data regularity.
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. We further extend our theory and formulate the requirements for correctly controlling a general loss function, such as the false negative proportion, with noisy labels. Our theory and experiments suggest that conformal prediction and risk-controlling techniques with noisy labels attain conservative risk over the clean ground truth labels whenever the noise is dispersive and increases variability. In other adversarial cases, we can also correct for noise of bounded size in the conformal prediction algorithm in order to ensure achieving the correct risk of the ground truth labels without score or data regularity.