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 …

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 …

In this work, we study the trade-off between differential privacy and adversarial robustness
under $ L_2 $-perturbations in the context of learning halfspaces. We prove nearly tight …

We revisit the fundamental problem of learning Axis-Aligned-Rectangles over a finite grid $
X^ d\subseteq\mathbb {R}^ d $ with differential privacy. Existing results show that the sample …

Let H be a class of boolean functions and consider a composed class H'that is derived from
H using some arbitrary aggregation rule (for example, H'may be the class of all 3-wise …

We consider the problem of online classification under a privacy constraint. In this setting a
learner observes sequentially a stream of labelled examples $(x_t, y_t) $, for $1\leq t\leq T …

We consider range queries on a graph under the constraints of differential privacy and query
ranges are defined as the set of edges on the shortest path of the graph. Edges in the graph …