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.
Convex formulation for learning from positive and unlabeled data
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2023 1verdicts
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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.