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In this work, we present a variety of novel information-theoreticgeneralization bounds for learning algorithms, from the supersample setting ofSteinke & Zakynthinou (2020)-the setting of the "conditional mutualinformation" framework. Our development exploits projecting the loss pair(obtained from a training instance and a testing instance) down to a singlenumber and correlating loss values with a Rademacher sequence (and its shiftedvariants). The presented bounds include square-root bounds, fast-rate bounds,including those based on variance and sharpness, and bounds for interpolatingalgorithms etc. We show theoretically or empirically that these bounds aretighter than all information-theoretic bounds known to date on the samesupersample setting.

Tighter Information-Theoretic Generalization Bounds from Supersamples