We study the task of differentially private clustering. For several basic clustering problems,
including Euclidean DensestBall, 1-Cluster, k-means, and k-median, we give efficient …

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 study the problem of $(\epsilon,\delta) $-differentially private learning of linear predictors
with convex losses. We provide results for two subclasses of loss functions. The first case is …

In Semi-Supervised Semi-Private (SP) learning, the learner has access to both public
unlabelled and private labelled data. We propose PILLAR, an easy-to-implement and …

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

We present a series of new differentially private (DP) algorithms with dimension-
independent margin guarantees. For the family of linear hypotheses, we give a pure DP …

We present a private agnostic learner for halfspaces over an arbitrary finite domain $
X\subset\R^ d $ with sample complexity $\mathsf {poly}(d, 2^{\log^*| X|}) $. The building …

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 study the relationship between the notions of differentially private learning and online
learning. Several recent works have shown that differentially private learning implies online …

In this paper, we study the problem of PAC learning halfspaces in the non-interactive local
differential privacy model (NLDP). To breach the barrier of exponential sample complexity …