Marginal coverage

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Noise-Adaptive Conformal Classification With Marginal Coverage

A revised arXiv paper proposes a noise-adaptive approach to conformal inference for classification. Standard conformal methods give prediction sets with guaranteed coverage, but the work addresses how that reliability holds when label or feature noise is present. The method targets marginal coverage guarantees while adjusting to noisy data conditions.

conformal-prediction/uncertainty/classification/arxiv