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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.
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“Learning from noisy labels with deep neural networks: A survey”
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“With Malice Toward None: Assessing Uncertainty via Equalized Coverage”
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“Conformal Prediction is Robust to Label Noise”
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“Prediction and outlier detection in classification problems”
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