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 …

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 …

This paper is concerned with pool-based active learning for deep neural networks.
Motivated by coreset dataset compression ideas, we present a novel active learning …

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 …

A seminal result in learning theory characterizes the PAC learnability of binary classes
through the Vapnik-Chervonenkis dimension. Extending this characterization to the general …

The Normalized Compression Distance (NCD) has been used in a number of domains to
compare objects with varying feature types. This flexibility comes from the use of general …

We extend the classical theory of PAC learning in a way which allows to model a rich variety
of practical learning tasks where the data satisfy special properties that ease the learning …

We review autoregressive predictive coding (APC), an approach to learn speech
representation by predicting a future frame given the past frames. We present three different …

We present a minimax optimal learner for the problem of learning predictors robust to
adversarial examples at test-time. Interestingly, we find that this requires new algorithmic …