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Robustness Implies Privacy in Statistical Estimation

9 December 2022

Papers citing "Robustness Implies Privacy in Statistical Estimation"

38 / 38 papers shown

Title
Private Statistical Estimation via Truncation Manolis Zampetakis Felix Zhou 92 0 0 18 May 2025
Causality Is Key to Understand and Balance Multiple Goals in Trustworthy ML and Foundation Models Ruta Binkyte Ivaxi Sheth Zhijing Jin Mohammad Havaei Bernhard Schölkopf Mario Fritz 532 1 0 28 Feb 2025
Optimal Rates for Robust Stochastic Convex Optimization Changyu Gao Andrew Lowy Xingyu Zhou Stephen J. Wright 121 0 0 15 Dec 2024
From Robustness to Privacy and Back Hilal Asi Jonathan R. Ullman Lydia Zakynthinou 83 30 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 73 21 0 28 Jan 2023
A Fast Algorithm for Adaptive Private Mean Estimation John C. Duchi Saminul Haque Rohith Kuditipudi FedML 62 15 0 17 Jan 2023
Privacy Induces Robustness: Information-Computation Gaps and Sparse Mean Estimation Kristian Georgiev Samuel B. Hopkins FedML 90 24 0 01 Nov 2022
Differentially private multivariate medians Kelly Ramsay Aukosh Jagannath Shojaéddin Chenouri 51 4 0 12 Oct 2022
New Lower Bounds for Private Estimation and a Generalized Fingerprinting Lemma Gautam Kamath Argyris Mouzakis Vikrant Singhal FedML 77 30 0 17 May 2022
On robustness and local differential privacy Mengchu Li Thomas B. Berrett Yi Yu 63 26 0 03 Jan 2022
Private Robust Estimation by Stabilizing Convex Relaxations Pravesh Kothari Pasin Manurangsi A. Velingker 66 47 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 74 62 0 25 Nov 2021
Private and polynomial time algorithms for learning Gaussians and beyond H. Ashtiani Christopher Liaw 114 48 0 22 Nov 2021
Differential privacy and robust statistics in high dimensions Xiyang Liu Weihao Kong Sewoong Oh 50 69 0 12 Nov 2021
A Private and Computationally-Efficient Estimator for Unbounded Gaussians Gautam Kamath Argyris Mouzakis Vikrant Singhal Thomas Steinke Jonathan R. Ullman 95 40 0 08 Nov 2021
Tight and Robust Private Mean Estimation with Few Users Cheng-Han Chiang Vahab Mirrokni Hung-yi Lee FedML 72 30 0 22 Oct 2021
Polynomial-Time Sum-of-Squares Can Robustly Estimate Mean and Covariance of Gaussians Optimally Pravesh Kothari Peter Manohar Brian Hu Zhang 70 17 0 22 Oct 2021
Perturbed M-Estimation: A Further Investigation of Robust Statistics for Differential Privacy Aleksandra B. Slavkovic Roberto Molinari 47 5 0 05 Aug 2021
Covariance-Aware Private Mean Estimation Without Private Covariance Estimation Gavin Brown Marco Gaboardi Adam D. Smith Jonathan R. Ullman Lydia Zakynthinou FedML 84 50 0 24 Jun 2021
Robust and Differentially Private Mean Estimation Xiyang Liu Weihao Kong Sham Kakade Sewoong Oh OOD FedML 84 77 0 18 Feb 2021
Differentially private depth functions and their associated medians Kelly Ramsay Shojaéddin Chenouri FedML 48 8 0 07 Jan 2021
Robust and Private Learning of Halfspaces Badih Ghazi Ravi Kumar Pasin Manurangsi Thao Nguyen 62 12 0 30 Nov 2020
On the Sample Complexity of Privately Learning Unbounded High-Dimensional Gaussians Ishaq Aden-Ali H. Ashtiani Gautam Kamath 118 44 0 19 Oct 2020
Private Mean Estimation of Heavy-Tailed Distributions Gautam Kamath Vikrant Singhal Jonathan R. Ullman 87 100 0 21 Feb 2020
Privacy-preserving parametric inference: a case for robust statistics Marco Avella-Medina 45 51 0 22 Nov 2019
Recent Advances in Algorithmic High-Dimensional Robust Statistics Ilias Diakonikolas D. Kane OOD 81 183 0 14 Nov 2019
Average-Case Averages: Private Algorithms for Smooth Sensitivity and Mean Estimation Mark Bun Thomas Steinke 75 76 0 06 Jun 2019
Private Hypothesis Selection Mark Bun Gautam Kamath Thomas Steinke Zhiwei Steven Wu 79 91 0 30 May 2019
How Hard Is Robust Mean Estimation? Samuel B. Hopkins Jerry Li 36 37 0 19 Mar 2019
High-dimensional estimation via sum-of-squares proofs P. Raghavendra T. Schramm David Steurer 51 59 0 30 Jul 2018
Efficient Algorithms and Lower Bounds for Robust Linear Regression Ilias Diakonikolas Weihao Kong Alistair Stewart 92 164 0 31 May 2018
Privately Learning High-Dimensional Distributions Gautam Kamath Jerry Li Vikrant Singhal Jonathan R. Ullman FedML 102 151 0 01 May 2018
Efficient Algorithms for Outlier-Robust Regression Adam R. Klivans Pravesh Kothari Raghu Meka AAML 60 157 0 08 Mar 2018
Finite Sample Differentially Private Confidence Intervals Vishesh Karwa Salil P. Vadhan 72 195 0 10 Nov 2017
Resilience: A Criterion for Learning in the Presence of Arbitrary Outliers Jacob Steinhardt Moses Charikar Gregory Valiant 90 140 0 15 Mar 2017
Being Robust (in High Dimensions) Can Be Practical Ilias Diakonikolas Gautam Kamath D. Kane Jerry Li Ankur Moitra Alistair Stewart 123 255 0 02 Mar 2017
Statistical Query Lower Bounds for Robust Estimation of High-dimensional Gaussians and Gaussian Mixtures Ilias Diakonikolas D. Kane Alistair Stewart 92 233 0 10 Nov 2016
On the Geometry of Differential Privacy Moritz Hardt Kunal Talwar 135 464 0 21 Jul 2009