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A Conformal Prediction Score that is Robust to Label Noise

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arxiv 2405.02648 v2 pith:7E2XKEHE submitted 2024-05-04 cs.LG cs.AIcs.CV

Coby Penso , Jacob Goldberger This is my paper · ORCID
classification cs.LGcs.AIcs.CV
keywords predictionconformalscorenoiselabelnoise-freenoisyrobust
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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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Robust Conformalized Selection with Noisy Responses

    stat.ML 2026-07 accept novelty 7.0 of 10

    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.

  2. Robust Bayes-Assisted Conformal Prediction

    stat.ML 2026-07 accept novelty 7.0 of 10

    Heavy-tailed and empirical-Bayes residual scores adaptively interpolate between DTO and DTA, yielding tighter conformal intervals under mean shift without sacrificing coverage.

  3. How Many Iterations to Jailbreak? Dynamic Budget Allocation for Multi-Turn LLM Evaluation

    cs.LG 2026-05 unverdicted novelty 7.0 of 10

    DAPRO provides the first dynamic, theoretically guaranteed way to allocate interaction budgets across test cases for bounding time-to-event in multi-turn LLM evaluations, achieving tighter coverage than static conform...

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