A unified data-processing framework produces tighter change-of-measure inequalities that improve information-theoretic generalization bounds across learning theory and privacy.
Controlling bias in adaptive data analysis using information theory,
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Sharper information-theoretic generalization bounds for differentially private algorithms obtained via typicality arguments that improve prior mutual-information results and add new maximal-leakage bounds.
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Tighter Information-Theoretic Generalization Bounds via a Novel Class of Change of Measure Inequalities
A unified data-processing framework produces tighter change-of-measure inequalities that improve information-theoretic generalization bounds across learning theory and privacy.
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On the Generalization Error of Differentially Private Algorithms via Typicality
Sharper information-theoretic generalization bounds for differentially private algorithms obtained via typicality arguments that improve prior mutual-information results and add new maximal-leakage bounds.