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Fast, Sample-Efficient, Affine-Invariant Private Mean and Covariance
  Estimation for Subgaussian Distributions

Fast, Sample-Efficient, Affine-Invariant Private Mean and Covariance Estimation for Subgaussian Distributions

28 January 2023
Gavin Brown
Samuel B. Hopkins
Adam D. Smith
    FedML
ArXivPDFHTML

Papers citing "Fast, Sample-Efficient, Affine-Invariant Private Mean and Covariance Estimation for Subgaussian Distributions"

5 / 5 papers shown
Title
On Differentially Private U Statistics
On Differentially Private U Statistics
Kamalika Chaudhuri
Po-Ling Loh
Shourya Pandey
Purnamrita Sarkar
FedML
54
0
0
06 Jul 2024
New Lower Bounds for Private Estimation and a Generalized Fingerprinting
  Lemma
New Lower Bounds for Private Estimation and a Generalized Fingerprinting Lemma
Gautam Kamath
Argyris Mouzakis
Vikrant Singhal
FedML
24
26
0
17 May 2022
A Private and Computationally-Efficient Estimator for Unbounded
  Gaussians
A Private and Computationally-Efficient Estimator for Unbounded Gaussians
Gautam Kamath
Argyris Mouzakis
Vikrant Singhal
Thomas Steinke
Jonathan R. Ullman
50
39
0
08 Nov 2021
On the Sample Complexity of Privately Learning Unbounded
  High-Dimensional Gaussians
On the Sample Complexity of Privately Learning Unbounded High-Dimensional Gaussians
Ishaq Aden-Ali
H. Ashtiani
Gautam Kamath
32
41
0
19 Oct 2020
Privately Learning High-Dimensional Distributions
Privately Learning High-Dimensional Distributions
Gautam Kamath
Jerry Li
Vikrant Singhal
Jonathan R. Ullman
FedML
62
148
0
01 May 2018
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