Quantum learning theory is a new and very active area of research at the intersection of
quantum computing and machine learning. Important breakthroughs in the past two years …

When two different parties use the same learning rule on their own data, how can we test
whether the distributions of the two outcomes are similar? In this paper, we study the …

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 …

Most works in learning with differential privacy (DP) have focused on the setting where each
user has a single sample. In this work, we consider the setting where each user holds $ m …

We show that many definitions of stability found in the learning theory literature are
equivalent to one another. We distinguish between two families of definitions of stability …

We prove novel algorithmic guarantees for several online problems in the smoothed
analysis model. In this model, at each time step an adversary chooses an input distribution …

As machine learning algorithms are deployed on sensitive data in critical decision making
processes, it is becoming increasingly important that they are also private and fair. In this …

We introduce the notion of a reproducible algorithm in the context of learning. A reproducible
learning algorithm is resilient to variations in its samples—with high probability, it returns the …

We examine the relationship between the mutual information between the output model and
the empirical sample and the algorithm's generalization in the context of stochastic convex …

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 …