A unified data-processing framework produces tighter change-of-measure inequalities that improve information-theoretic generalization bounds across learning theory and privacy.
Upper bounds on the generalization error of private algorithms for discrete data,
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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.