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.
Detecting and correcting for label shift with black box predictors
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
Proposes meta-learning attack with priority-aware gradient alignment for sample-wise targeted attacks on TTA that maintain label distribution consistency with no-attack baseline.
In the Gaussian case, invariant features predicting Y independent of confounders Z are given by the top d eigenvectors of a matrix derived from the optimal transport barycenter of Z given Y.
citing papers explorer
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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.
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Sample-wise Targeted Adversarial Attacks on Test-time Adaptation
Proposes meta-learning attack with priority-aware gradient alignment for sample-wise targeted attacks on TTA that maintain label distribution consistency with no-attack baseline.
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Invariant Feature Extraction Through Conditional Independence and the Optimal Transport Barycenter Problem: the Gaussian case
In the Gaussian case, invariant features predicting Y independent of confounders Z are given by the top d eigenvectors of a matrix derived from the optimal transport barycenter of Z given Y.