We give the first polynomial-time algorithm to estimate the mean of ad-variate probability
distribution with bounded covariance from Õ (d) independent samples subject to pure …

We give the first polynomial time and sample (epsilon, delta)-differentially private (DP)
algorithm to estimate the mean, covariance and higher moments in the presence of a …

We study the problem of private distribution learning with access to public data. In this setup,
which we refer to as* public-private learning*, the learner is given public and private …

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 …

We present a fairly general framework for reducing $(\varepsilon,\delta) $-differentially
private (DP) statistical estimation to its non-private counterpart. As the main application of …

We introduce general tools for designing efficient private estimation algorithms, in the high-
dimensional settings, whose statistical guarantees almost match those of the best known …

We initiate the study of differentially private (DP) estimation with access to a small amount of
public data. For private estimation of $ d $-dimensional Gaussians, we assume that the …

We present simple differentially private estimators for the parameters of multivariate sub-
Gaussian data that are accurate at small sample sizes. We demonstrate the effectiveness of …

We give the first polynomial-time, polynomial-sample, differentially private estimator for the
mean and covariance of an arbitrary Gaussian distribution $ N (\mu,\Sigma) $ in $\R^ d $. All …

We give new upper and lower bounds on the minimax sample complexity of differentially
private mean estimation of distributions with bounded $ k $-th moments. Roughly speaking …