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 …

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 …

Previous work on user-level differential privacy (DP)[Ghazi et al. NeurIPS 2021, Bun et al.
STOC 2023] obtained generic algorithms that work for various learning tasks. However, their …

We prove that every concept class with finite Littlestone dimension can be learned by an
(approximate) differentially-private algorithm. This answers an open question of Alon et …

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 …

Let H be a binary-labeled concept class. We prove that H can be PAC learned by an
(approximate) differentially private algorithm if and only if it has a finite Littlestone dimension …

We use and adapt the Borsuk-Ulam Theorem from topology to derive limitations on list-
replicable and globally stable learning algorithms. We further demonstrate the applicability …

We continue the study of the computational complexity of differentially private PAC learning
and how it is situated within the foundations of machine learning. A recent line of work …

The notion of replicable algorithms was introduced by Impagliazzo, Lei, Pitassi, and Sorrell
(STOC'22) to describe randomized algorithms that are stable under the resampling of their …

Learning an unknown n-qubit quantum state rho is a fundamental challenge in quantum
computing. Information-theoretically, it is known that tomography requires exponential in n …