5 papers
Actually, All Learnable Distribution Classes Are Privately Learnable
Mohammad Afzali, Hassan Ashtiani, Alex Bie +4
We show that every class of distributions that is learnable up to arbitrarily small target error in total variation distance with finitely many samples is also learnable under ($\v…
Private Evaluation of High-Dimensional Functions
Mohammad Afzali, Hassan Ashtiani, Adam Smith
We study differentially private black-box evaluation of arbitrary high-dimensional functions. Given a dataset and query access to a function ---whose sensitivity may be unkn…
Efficient Agnostic Learning of Univariate Densities Under Local Differential Privacy
Mohammad Afzali, Hassan Ashtiani, Sivan Sabato
We propose a general framework for non-interactive locally differentially private (LDP) learning of structured univariate distributions that can be approximated by piece-wise polyn…
Agnostic Private Density Estimation for GMMs via List Global Stability
Mohammad Afzali, Hassan Ashtiani, Christopher Liaw
We consider the problem of private density estimation for mixtures of unrestricted high dimensional Gaussians in the agnostic setting. We prove the first upper bound on the sample…
Mixtures of Gaussians are Privately Learnable with a Polynomial Number of Samples
Mohammad Afzali, Hassan Ashtiani, Christopher Liaw
We study the problem of estimating mixtures of Gaussians under the constraint of differential privacy (DP). Our main result is that s…