A Polynomial Time, Pure Differentially Private Estimator for Binary Product Distributions

13 April 2023

Papers citing "A Polynomial Time, Pure Differentially Private Estimator for Binary Product Distributions"

12 / 12 papers shown

Title
Learning with Differentially Private (Sliced) Wasserstein Gradients Clément Lalanne Jean-Michel Loubes David Rodríguez-Vítores FedML 46 0 0 03 Feb 2025
Privately Learning Smooth Distributions on the Hypercube by Projections Clément Lalanne Sébastien Gadat 38 1 0 16 Sep 2024
Private Means and the Curious Incident of the Free Lunch Jack Fitzsimons James Honaker Michael Shoemate Vikrant Singhal 46 2 0 19 Aug 2024
Sample-Optimal Locally Private Hypothesis Selection and the Provable Benefits of Interactivity A. F. Pour Hassan Ashtiani S. Asoodeh 41 0 0 09 Dec 2023
About the Cost of Central Privacy in Density Estimation Clément Lalanne Aurélien Garivier Rémi Gribonval 25 3 0 26 Jun 2023
Continual Mean Estimation Under User-Level Privacy Anand George Lekshmi Ramesh A. V. Singh Himanshu Tyagi FedML 36 8 0 20 Dec 2022
Privacy Induces Robustness: Information-Computation Gaps and Sparse Mean Estimation Kristian Georgiev Samuel B. Hopkins FedML 36 21 0 01 Nov 2022
New Lower Bounds for Private Estimation and a Generalized Fingerprinting Lemma Gautam Kamath Argyris Mouzakis Vikrant Singhal FedML 42 26 0 17 May 2022
A Private and Computationally-Efficient Estimator for Unbounded Gaussians Gautam Kamath Argyris Mouzakis Vikrant Singhal Thomas Steinke Jonathan R. Ullman 55 39 0 08 Nov 2021
Robust and Differentially Private Mean Estimation Xiyang Liu Weihao Kong Sham Kakade Sewoong Oh OOD FedML 53 75 0 18 Feb 2021
On the Sample Complexity of Privately Learning Unbounded High-Dimensional Gaussians Ishaq Aden-Ali H. Ashtiani Gautam Kamath 40 41 0 19 Oct 2020
Privately Learning High-Dimensional Distributions Gautam Kamath Jerry Li Vikrant Singhal Jonathan R. Ullman FedML 69 148 0 01 May 2018