6 citations · 12 across the 7 of their papers we have counts for
9 papers
Efficient and Optimal Learning of Discrete Distributions with Person-Level Privacy
Gautam Kamath, Mahbod Majid, Ankur Moitra +2
We study learning a discrete distribution under person-level differential privacy in the batched setting. Given users, each contributing i.i.d. samples from an unknown dist…
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…
Robust Statistical Estimators with Bounded Empirical Sensitivity
Valentio Iverson, Gautam Kamath, Argyris Mouzakis +1
We introduce a new measure of robustness for statistical estimators, which we call \emph{empirical sensitivity}. An estimator has bounded empirical sensitivity if, with hi…
Optimal Differentially Private Sampling of Unbounded Gaussians
Valentio Iverson, Gautam Kamath, Argyris Mouzakis
We provide the first -sample algorithm for sampling from unbounded Gaussian distributions under the constraint of $\left(\varepsilon, δ\right…
Private Mean Estimation with Person-Level Differential Privacy
Sushant Agarwal, Gautam Kamath, Mahbod Majid +3
We study person-level differentially private (DP) mean estimation in the case where each person holds multiple samples. DP here requires the usual notion of distributional stabilit…
Not All Learnable Distribution Classes are Privately Learnable
Mark Bun, Gautam Kamath, Argyris Mouzakis +1
We give an example of a class of distributions that is learnable up to constant error in total variation distance with a finite number of samples, but not learnable under $(\vareps…
A Bias-Accuracy-Privacy Trilemma for Statistical Estimation
Gautam Kamath, Argyris Mouzakis, Matthew Regehr +3
Differential privacy (DP) is a rigorous notion of data privacy, used for private statistics. The canonical algorithm for differentially private mean estimation is to first clip the…
New Lower Bounds for Private Estimation and a Generalized Fingerprinting Lemma
Gautam Kamath, Argyris Mouzakis, Vikrant Singhal
We prove new lower bounds for statistical estimation tasks under the constraint of -differential privacy. First, we provide tight lower bounds for private covaria…
A Private and Computationally-Efficient Estimator for Unbounded Gaussians
Gautam Kamath, Argyris Mouzakis, Vikrant Singhal +2
We give the first polynomial-time, polynomial-sample, differentially private estimator for the mean and covariance of an arbitrary Gaussian distribution in $\mat…