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A Conformal Prediction Score that is Robust to Label Noise
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A Conformal Prediction Score that is Robust to Label Noise
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Conformal Prediction (CP) quantifies network uncertainty by building a small prediction set with a pre-defined probability that the correct class is within this set. In this study we tackle the problem of CP calibration based on a validation set with noisy labels. We introduce a conformal score that is robust to label noise. The noise-free conformal score is estimated using the noisy labeled data and the noise level. In the test phase the noise-free score is used to form the prediction set. We applied the proposed algorithm to several standard medical imaging classification datasets. We show that our method outperforms current methods by a large margin, in terms of the average size of the prediction set, while maintaining the required coverage.
Forward citations
Cited by 6 Pith papers
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Robust Conformalized Selection with Noisy Responses
RCS uses class-conditioned reweighting of noisy calibration data to control the false discovery rate in conformalized selection tasks, with asymptotic guarantees and empirical gains over prior methods.
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Heavy-tailed and empirical-Bayes residual scores adaptively interpolate between DTO and DTA, yielding tighter conformal intervals under mean shift without sacrificing coverage.
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How Many Iterations to Jailbreak? Dynamic Budget Allocation for Multi-Turn LLM Evaluation
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When Does Trimming Help Conformal Prediction? A Retained-Law Diagnostic under Calibration Contamination
Trimming helps conformal prediction under contamination precisely when the anomaly score separates retention probabilities without biasing clean scores, otherwise the retained mixture coefficient prevents substantial ...
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How Many Iterations to Jailbreak? Dynamic Budget Allocation for Multi-Turn LLM Evaluation
DAPRO dynamically allocates multi-turn LLM evaluation budget to produce valid, tighter lower bounds on iterations-to-event without the conditional-independence assumption of prior conformal survival methods.
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