This monograph portrays optimization as a process. In many practical applications the
environment is so complex that it is infeasible to lay out a comprehensive theoretical model …

In this work, we aim to characterize the statistical complexity of realizable regression both in
the PAC learning setting and the online learning setting. Previous work had established the …

Deriving generalization bounds for stable algorithms is a classical question in learning
theory taking its roots in the early works by Vapnik and Chervonenkis (1974) and Rogers …

In this work, we investigate the expressiveness of the" conditional mutual information"(CMI)
framework of Steinke and Zakynthinou (2020) and the prospect of using it to provide a …

The sharpest known high probability generalization bounds for uniformly stable algorithms
(Feldman, Vondrak, NeurIPS 2018, COLT, 2019),(Bousquet, Klochkov, Zhivotovskiy, COLT …

We present new information-theoretic generalization guarantees through the a novel
construction of the" neighboring-hypothesis" matrix and a new family of stability notions …

A major challenge in designing efficient statistical supervised learning algorithms is finding
representations that perform well not only on available training samples but also on unseen …

A compression function is a map that slims down an observational set into a subset of
reduced size, while preserving its informational content. In multiple applications, the …

We study the problem of learning predictors that are robust to adversarial examples with
respect to an unknown perturbation set, relying instead on interaction with an adversarial …

Generalization bounds are valuable both for theory and applications. On the one hand, they
shed light on the mechanisms that underpin the learning processes; on the other, they certify …