On the Sample Complexity of Privately Learning Unbounded High-Dimensional Gaussians

19 October 2020

Papers citing "On the Sample Complexity of Privately Learning Unbounded High-Dimensional Gaussians"

37 / 37 papers shown

Title
Private Training & Data Generation by Clustering Embeddings Felix Y. Zhou Samson Zhou Vahab Mirrokni Alessandro Epasto Vincent Cohen-Addad 74 0 0 20 Jun 2025
Nearly-Linear Time Private Hypothesis Selection with the Optimal Approximation Factor Maryam Aliakbarpour Zhan Shi Ria Stevens Vincent X. Wang 76 1 0 01 Jun 2025
A Private Approximation of the 2nd-Moment Matrix of Any Subsamplable Input Bar Mahpud Or Sheffet 52 0 0 20 May 2025
Optimal Differentially Private Sampling of Unbounded Gaussians Valentio Iverson Gautam Kamath Argyris Mouzakis 142 0 0 03 Mar 2025
Learning with Differentially Private (Sliced) Wasserstein Gradients David Rodríguez-Vítores Clément Lalanne Jean-Michel Loubes FedML 161 0 0 03 Feb 2025
Dimension-free Private Mean Estimation for Anisotropic Distributions Yuval Dagan Michael I. Jordan Xuelin Yang Lydia Zakynthinou Nikita Zhivotovskiy 186 2 0 01 Nov 2024
Privately Learning Smooth Distributions on the Hypercube by Projections Clément Lalanne Sébastien Gadat 130 1 0 16 Sep 2024
Distribution Learnability and Robustness Shai Ben-David Alex Bie Gautam Kamath Tosca Lechner 139 3 0 25 Jun 2024
Differentially Private Post-Processing for Fair Regression Ruicheng Xian Qiaobo Li Gautam Kamath Han Zhao 129 4 0 07 May 2024
Lower Bounds for Private Estimation of Gaussian Covariance Matrices under All Reasonable Parameter Regimes V. S. Portella Nick Harvey 95 9 0 26 Apr 2024
Insufficient Statistics Perturbation: Stable Estimators for Private Least Squares Gavin Brown J. Hayase Samuel B. Hopkins Weihao Kong Xiyang Liu Sewoong Oh Juan C. Perdomo Adam D. Smith 87 2 0 23 Apr 2024
Differentially private projection-depth-based medians Kelly Ramsay Dylan Spicker 68 2 0 12 Dec 2023
Sample-Optimal Locally Private Hypothesis Selection and the Provable Benefits of Interactivity A. F. Pour Hassan Ashtiani S. Asoodeh 125 2 0 09 Dec 2023
Instance-Specific Asymmetric Sensitivity in Differential Privacy David Durfee 116 1 0 02 Nov 2023
Better and Simpler Lower Bounds for Differentially Private Statistical Estimation Shyam Narayanan FedML 112 13 0 10 Oct 2023
Mixtures of Gaussians are Privately Learnable with a Polynomial Number of Samples Mohammad Afzali H. Ashtiani Christopher Liaw 146 7 0 07 Sep 2023
Private Distribution Learning with Public Data: The View from Sample Compression Shai Ben-David Alex Bie C. Canonne Gautam Kamath Vikrant Singhal 120 15 0 11 Aug 2023
A Polynomial Time, Pure Differentially Private Estimator for Binary Product Distributions Vikrant Singhal 182 9 0 13 Apr 2023
Polynomial Time and Private Learning of Unbounded Gaussian Mixture Models Jamil Arbas H. Ashtiani Christopher Liaw 123 27 0 07 Mar 2023
From Robustness to Privacy and Back Hilal Asi Jonathan R. Ullman Lydia Zakynthinou 106 35 0 03 Feb 2023
Fast, Sample-Efficient, Affine-Invariant Private Mean and Covariance Estimation for Subgaussian Distributions Gavin Brown Samuel B. Hopkins Adam D. Smith FedML 120 22 0 28 Jan 2023
Privately Estimating a Gaussian: Efficient, Robust and Optimal Daniel Alabi Pravesh Kothari Pranay Tankala Prayaag Venkat Fred Zhang 156 0 0 15 Dec 2022
Robustness Implies Privacy in Statistical Estimation Samuel B. Hopkins Gautam Kamath Mahbod Majid Shyam Narayanan 174 60 0 09 Dec 2022
Private Estimation with Public Data Alex Bie Gautam Kamath Vikrant Singhal 114 34 0 16 Aug 2022
Differentially Private Covariance Revisited Wei Dong Yuting Liang K. Yi FedML 130 16 0 28 May 2022
New Lower Bounds for Private Estimation and a Generalized Fingerprinting Lemma Gautam Kamath Argyris Mouzakis Vikrant Singhal FedML 190 32 0 17 May 2022
Differentially Private Regression with Unbounded Covariates Jason Milionis Alkis Kalavasis Dimitris Fotakis Stratis Ioannidis 83 12 0 19 Feb 2022
Private Robust Estimation by Stabilizing Convex Relaxations Pravesh Kothari Pasin Manurangsi A. Velingker 86 50 0 07 Dec 2021
Efficient Mean Estimation with Pure Differential Privacy via a Sum-of-Squares Exponential Mechanism Samuel B. Hopkins Gautam Kamath Mahbod Majid FedML 106 64 0 25 Nov 2021
Private and polynomial time algorithms for learning Gaussians and beyond H. Ashtiani Christopher Liaw 166 50 0 22 Nov 2021
A Private and Computationally-Efficient Estimator for Unbounded Gaussians Gautam Kamath Argyris Mouzakis Vikrant Singhal Thomas Steinke Jonathan R. Ullman 122 42 0 08 Nov 2021
Universal Private Estimators Wei Dong K. Yi 136 21 0 04 Nov 2021
Covariance-Aware Private Mean Estimation Without Private Covariance Estimation Gavin Brown Marco Gaboardi Adam D. Smith Jonathan R. Ullman Lydia Zakynthinou FedML 157 50 0 24 Jun 2021
Privately Learning Mixtures of Axis-Aligned Gaussians Ishaq Aden-Ali H. Ashtiani Christopher Liaw FedML 100 13 0 03 Jun 2021
Robust and Differentially Private Mean Estimation Xiyang Liu Weihao Kong Sham Kakade Sewoong Oh OOD FedML 109 82 0 18 Feb 2021
Phase transitions for support recovery under local differential privacy C. Butucea A. Dubois Adrien Saumard FedML 116 3 0 30 Nov 2020
Private Hypothesis Selection Mark Bun Gautam Kamath Thomas Steinke Zhiwei Steven Wu 224 94 0 30 May 2019