A Private and Computationally-Efficient Estimator for Unbounded
Gaussians

A Private and Computationally-Efficient Estimator for Unbounded Gaussians

8 November 2021

Argyris Mouzakis

Vikrant Singhal

Jonathan R. Ullman

Papers citing "A Private and Computationally-Efficient Estimator for Unbounded Gaussians"

12 / 12 papers shown

Title
Private Means and the Curious Incident of the Free Lunch Jack Fitzsimons James Honaker Michael Shoemate Vikrant Singhal 37 2 0 19 Aug 2024
Sample-Optimal Locally Private Hypothesis Selection and the Provable Benefits of Interactivity A. F. Pour Hassan Ashtiani S. Asoodeh 38 0 0 09 Dec 2023
Instance-Specific Asymmetric Sensitivity in Differential Privacy David Durfee 21 1 0 02 Nov 2023
Private Distribution Learning with Public Data: The View from Sample Compression Shai Ben-David Alex Bie C. Canonne Gautam Kamath Vikrant Singhal 35 11 0 11 Aug 2023
Unbounded Differentially Private Quantile and Maximum Estimation D. Durfee 36 6 0 02 May 2023
Polynomial Time and Private Learning of Unbounded Gaussian Mixture Models Jamil Arbas H. Ashtiani Christopher Liaw 40 23 0 07 Mar 2023
Private Estimation with Public Data Alex Bie Gautam Kamath Vikrant Singhal 28 28 0 16 Aug 2022
New Lower Bounds for Private Estimation and a Generalized Fingerprinting Lemma Gautam Kamath Argyris Mouzakis Vikrant Singhal FedML 34 26 0 17 May 2022
FriendlyCore: Practical Differentially Private Aggregation Eliad Tsfadia E. Cohen Haim Kaplan Yishay Mansour Uri Stemmer 20 33 0 19 Oct 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