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Noisy-Label Calibration with Multiple Unreliable Labels

arXiv:2610.19543v1 Announce Type: new Abstract: In medical annotation and crowdsourcing, multiple annotators assign labels to each example, but these labels may be incorrect. Treating such noisy labels as ground truth can bias calibration evaluation. To address this problem, we propose methods for…

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  1. 2026-10-15 04:00 · arXiv stat.ML
    Noisy-Label Calibration with Multiple Unreliable Labels

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