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 the eyes of many control scientists, the theory of the scenario approach is a tool for
determining the sample size in certain randomized control-design methods, where an …

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 …

We study the question of learning an adversarially robust predictor. We show that any
hypothesis class $\mathcal {H} $ with finite VC dimension is robustly PAC learnable with …

We study the problem of private distribution learning with access to public data. In this setup,
which we refer to as* public-private learning*, the learner is given public and private …

From the winner of the Turing Award and the Abel Prize, an introduction to computational
complexity theory, its connections and interactions with mathematics, and its central role in …

The mathematical foundations of machine learning play a key role in the development of the
field. They improve our understanding and provide tools for designing new learning …

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 …

We study learning algorithms that are restricted to using a small amount of information from
their input sample. We introduce a category of learning algorithms we term {\em $ d $-bit …

Predictive maintenance, as one of the core components of Industry 4.0, takes a proactive
approach to maintain machines and systems in good order to keep downtime to a minimum …