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Conformal Prediction is Robust to Dispersive Label Noise
In such 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.
Training Uncertainty-Aware Classifiers with Conformalized Deep Learning
Deep neural networks are powerful tools to detect hidden patterns in data and leverage them to make predictions, but they are not designed to understand uncertainty and estimate reliable probabilities.
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