The paper introduces a Markov kernel framework for exhaustively classifying corruptions in supervised learning and derives loss corrections for label, attribute, and joint cases by comparing clean and corrupted Bayes risks.
Learning with noisy labels.Advances in neural information processing systems, 26, 2013
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Corruptions of Supervised Learning Problems: Typology and Mitigations
The paper introduces a Markov kernel framework for exhaustively classifying corruptions in supervised learning and derives loss corrections for label, attribute, and joint cases by comparing clean and corrupted Bayes risks.