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Classification

topic2 events
papersTODAY 04:00 UTC

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
papersSEP 11 04:00 UTC

arXiv Study Benchmarks Non-Conformity Score Functions for Conformal Prediction

A revised arXiv preprint surveys and compares non-conformity score functions used in conformal prediction for classification. Conformal prediction generates prediction sets rather than single labels, with guarantees that the sets cover the true class at a chosen rate. The work evaluates how different scoring choices affect the efficiency and validity of those sets.

conformal-prediction/benchmarking/uncertainty/machine-learning