In statistical learning and analysis from shared data, which is increasingly widely adopted in
platforms such as federated learning and meta-learning, there are two major concerns …

User language data can contain highly sensitive personal content. As such, it is imperative
to offer users a strong and interpretable privacy guarantee when learning from their data. In …

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 …

The problem of learning threshold functions is a fundamental one in machine learning.
Classical learning theory implies sample complexity of O (ξ− 1 log (1/β))(for generalization …

We provide efficient replicable algorithms for the problem of learning large-margin
halfspaces. Our results improve upon the algorithms provided by Impagliazzo, Lei, Pitassi …

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 …

In this paper we prove that the sample complexity of properly learning a class of Littlestone
dimension d with approximate differential privacy is Õ (d 6), ignoring privacy and accuracy …

The last few years have seen a surge of work on high dimensional statistics under privacy
constraints, mostly following two main lines of work: the" worst case" line, which does not …

Abstract Kasiviswanathan et al.(FOCS 2008) defined private learning as a combination of
PAC learning and differential privacy. Informally, a private learner is applied to a collection of …

We prove concentration inequalities for the output of the exponential mechanism about the
maximizer of the population objective function. This bound applies to objective functions that …