Private Robust Estimation by Stabilizing Convex Relaxations

7 December 2021

Papers citing "Private Robust Estimation by Stabilizing Convex Relaxations"

38 / 38 papers shown

Title
Optimal Differentially Private Sampling of Unbounded Gaussians Valentio Iverson Gautam Kamath Argyris Mouzakis 46 0 0 03 Mar 2025
SoS Certifiability of Subgaussian Distributions and its Algorithmic Applications Ilias Diakonikolas Samuel B. Hopkins Ankit Pensia Stefan Tiegel 45 3 0 28 Oct 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
Distribution Learnability and Robustness Shai Ben-David Alex Bie Gautam Kamath Tosca Lechner 34 2 0 25 Jun 2024
Perturb-and-Project: Differentially Private Similarities and Marginals Vincent Cohen-Addad Tommaso dÓrsi Alessandro Epasto Vahab Mirrokni Peilin Zhong 35 0 0 07 Jun 2024
Private Edge Density Estimation for Random Graphs: Optimal, Efficient and Robust Hongjie Chen Jingqiu Ding Yiding Hua David Steurer 33 1 0 26 May 2024
On Differentially Private Subspace Estimation in a Distribution-Free Setting Eliad Tsfadia 25 1 0 09 Feb 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
Instance-Specific Asymmetric Sensitivity in Differential Privacy David Durfee 32 1 0 02 Nov 2023
Statistical Barriers to Affine-equivariant Estimation Zihao Chen Yeshwanth Cherapanamjeri 56 0 0 16 Oct 2023
Better and Simpler Lower Bounds for Differentially Private Statistical Estimation Shyam Narayanan FedML 19 10 0 10 Oct 2023
Mixtures of Gaussians are Privately Learnable with a Polynomial Number of Samples Mohammad Afzali H. Ashtiani Christopher Liaw 32 5 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 42 11 0 11 Aug 2023
PLAN: Variance-Aware Private Mean Estimation Martin Aumüller C. Lebeda Boel Nelson Rasmus Pagh FedML 26 4 0 14 Jun 2023
Robust and differentially private stochastic linear bandits Vasileios Charisopoulos Hossein Esfandiari Vahab Mirrokni FedML 26 1 0 23 Apr 2023
A Polynomial Time, Pure Differentially Private Estimator for Binary Product Distributions Vikrant Singhal 34 9 0 13 Apr 2023
Polynomial Time and Private Learning of Unbounded Gaussian Mixture Models Jamil Arbas H. Ashtiani Christopher Liaw 40 23 0 07 Mar 2023
From Robustness to Privacy and Back Hilal Asi Jonathan R. Ullman Lydia Zakynthinou 44 27 0 03 Feb 2023
Near Optimal Private and Robust Linear Regression Xiyang Liu Prateek Jain Weihao Kong Sewoong Oh A. Suggala 41 9 0 30 Jan 2023
Fast, Sample-Efficient, Affine-Invariant Private Mean and Covariance Estimation for Subgaussian Distributions Gavin Brown Samuel B. Hopkins Adam D. Smith FedML 27 17 0 28 Jan 2023
A Fast Algorithm for Adaptive Private Mean Estimation John C. Duchi Saminul Haque Rohith Kuditipudi FedML 24 15 0 17 Jan 2023
Private estimation algorithms for stochastic block models and mixture models Hongjie Chen Vincent Cohen-Addad Tommaso dÓrsi Alessandro Epasto Jacob Imola David Steurer Stefan Tiegel FedML 43 20 0 11 Jan 2023
Privately Estimating a Gaussian: Efficient, Robust and Optimal Daniel Alabi Pravesh Kothari Pranay Tankala Prayaag Venkat Fred Zhang 66 0 0 15 Dec 2022
Robustness Implies Privacy in Statistical Estimation Samuel B. Hopkins Gautam Kamath Mahbod Majid Shyam Narayanan 18 49 0 09 Dec 2022
Privacy Induces Robustness: Information-Computation Gaps and Sparse Mean Estimation Kristian Georgiev Samuel B. Hopkins FedML 36 21 0 01 Nov 2022
Private Estimation with Public Data Alex Bie Gautam Kamath Vikrant Singhal 36 28 0 16 Aug 2022
Differentially Private Covariance Revisited Wei Dong Yuting Liang K. Yi FedML 23 13 0 28 May 2022
DP-PCA: Statistically Optimal and Differentially Private PCA Xiyang Liu Weihao Kong Prateek Jain Sewoong Oh 38 21 0 27 May 2022
Differentially Private Regression with Unbounded Covariates Jason Milionis Alkis Kalavasis Dimitris Fotakis Stratis Ioannidis 23 10 0 19 Feb 2022
On robustness and local differential privacy Mengchu Li Thomas B. Berrett Yi Yu 24 24 0 03 Jan 2022
Private and polynomial time algorithms for learning Gaussians and beyond H. Ashtiani Christopher Liaw 60 44 0 22 Nov 2021
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
Universal Private Estimators Wei Dong K. Yi 24 19 0 04 Nov 2021
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
Prochlo: Strong Privacy for Analytics in the Crowd Andrea Bittau Ulfar Erlingsson Petros Maniatis Ilya Mironov A. Raghunathan David Lie Mitch Rudominer Ushasree Kode J. Tinnés B. Seefeld 91 278 0 02 Oct 2017