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A unified framework for information-theoretic generalization bounds
This paper presents a general methodology for deriving information-theoretic generalization bounds for learning algorithms.
Majorizing Measures, Codes, and Information
The majorizing measure theorem of Fernique and Talagrand is a fundamental result in the theory of random processes.
A Chain Rule for the Expected Suprema of Bernoulli Processes
We obtain an upper bound on the expected supremum of a Bernoulli process indexed by the image of an index set under a uniformly Lipschitz function class in terms of properties of the index set and the function class, extending an earlier result of Maurer for Gaussian processes.
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