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Robustness Implies Privacy in Statistical Estimation

Symposium on the Theory of Computing (STOC), 2022

9 December 2022

Papers citing "Robustness Implies Privacy in Statistical Estimation"

36 / 36 papers shown

Title
Robust Estimation Under Heterogeneous Corruption Rates Syomantak Chaudhuri Jerry Li T. Courtade FedML 120 0 0 20 Aug 2025
Private Training & Data Generation by Clustering Embeddings Felix Y. Zhou Samson Zhou Vahab Mirrokni Alessandro Epasto Vincent Cohen-Addad 186 0 0 20 Jun 2025
Square $χ$ PO: Differentially Private and Robust $χ^2$ -Preference Optimization in Offline Direct Alignment Xingyu Zhou Yulian Wu Wenqian Weng Francesco Orabona 301 0 0 27 May 2025
Private Statistical Estimation via Truncation Manolis Zampetakis Felix Zhou 234 0 0 18 May 2025
Sample-Optimal Private Regression in Polynomial TimeSymposium on the Theory of Computing (STOC), 2025 Prashanti Anderson Ainesh Bakshi Mahbod Majid Stefan Tiegel 120 1 0 31 Mar 2025
Optimal Differentially Private Sampling of Unbounded GaussiansAnnual Conference Computational Learning Theory (COLT), 2025 Valentio Iverson Gautam Kamath Argyris Mouzakis 307 1 0 03 Mar 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 1.2K 5 0 28 Feb 2025
Tukey Depth Mechanisms for Practical Private Mean Estimation Gavin Brown Lydia Zakynthinou 182 0 0 25 Feb 2025
SoS Certificates for Sparse Singular Values and Their Applications: Robust Statistics, Subspace Distortion, and MoreSymposium on the Theory of Computing (STOC), 2024 Ilias Diakonikolas Samuel B. Hopkins Ankit Pensia Stefan Tiegel 153 3 0 31 Dec 2024
Optimal Rates for Robust Stochastic Convex OptimizationSymposium on Foundations of Responsible Computing (FRC), 2024 Changyu Gao Andrew Lowy Xingyu Zhou Stephen J. Wright 439 0 0 15 Dec 2024
Sample-Efficient Private Learning of Mixtures of GaussiansNeural Information Processing Systems (NeurIPS), 2024 Hassan Ashtiani Mahbod Majid Shyam Narayanan FedML 127 0 0 04 Nov 2024
Dimension-free Private Mean Estimation for Anisotropic DistributionsNeural Information Processing Systems (NeurIPS), 2024 Yuval Dagan Michael I. Jordan Xuelin Yang Lydia Zakynthinou Nikita Zhivotovskiy 355 2 0 01 Nov 2024
Distribution Learnability and Robustness Shai Ben-David Alex Bie Gautam Kamath Tosca Lechner 312 4 0 25 Jun 2024
Perturb-and-Project: Differentially Private Similarities and MarginalsInternational Conference on Machine Learning (ICML), 2024 Vincent Cohen-Addad Tommaso dÓrsi Alessandro Epasto Vahab Mirrokni Peilin Zhong 359 1 0 07 Jun 2024
Private Edge Density Estimation for Random Graphs: Optimal, Efficient and Robust Hongjie Chen Jingqiu Ding Yiding Hua David Steurer 305 3 0 26 May 2024
Lower Bounds for Private Estimation of Gaussian Covariance Matrices under All Reasonable Parameter Regimes V. S. Portella Nick Harvey 223 9 0 26 Apr 2024
Private graphon estimation via sum-of-squaresSymposium on the Theory of Computing (STOC), 2024 Hongjie Chen Jingqiu Ding Tommaso dÓrsi Yiding Hua Chih-Hung Liu David Steurer 288 2 0 18 Mar 2024
General Inferential Limits Under Differential and Pufferfish PrivacyInternational Journal of Approximate Reasoning (IJAR), 2024 J. Bailie Ruobin Gong 285 2 0 27 Jan 2024
Sample-Optimal Locally Private Hypothesis Selection and the Provable Benefits of InteractivityAnnual Conference Computational Learning Theory (COLT), 2023 A. F. Pour Hassan Ashtiani S. Asoodeh 227 2 0 09 Dec 2023
The Bayesian Stability ZooNeural Information Processing Systems (NeurIPS), 2023 Shay Moran Hilla Schefler Jonathan Shafer 214 9 0 27 Oct 2023
Statistical Barriers to Affine-equivariant Estimation Zihao Chen Yeshwanth Cherapanamjeri 216 0 0 16 Oct 2023
Better and Simpler Lower Bounds for Differentially Private Statistical EstimationIEEE Transactions on Information Theory (IEEE Trans. Inf. Theory), 2023 Shyam Narayanan FedML 271 13 0 10 Oct 2023
Mixtures of Gaussians are Privately Learnable with a Polynomial Number of SamplesInternational Conference on Algorithmic Learning Theory (ALT), 2023 Mohammad Afzali H. Ashtiani Christopher Liaw 294 7 0 07 Sep 2023
Private Distribution Learning with Public Data: The View from Sample CompressionNeural Information Processing Systems (NeurIPS), 2023 Shai Ben-David Alex Bie C. Canonne Gautam Kamath Vikrant Singhal 298 16 0 11 Aug 2023
The Full Landscape of Robust Mean Testing: Sharp Separations between Oblivious and Adaptive ContaminationIEEE Annual Symposium on Foundations of Computer Science (FOCS), 2023 C. Canonne Samuel B. Hopkins Jungshian Li Allen Liu Shyam Narayanan AAML 209 8 0 18 Jul 2023
A Polynomial Time, Pure Differentially Private Estimator for Binary Product DistributionsInternational Conference on Algorithmic Learning Theory (ALT), 2023 Vikrant Singhal 529 9 0 13 Apr 2023
Polynomial Time and Private Learning of Unbounded Gaussian Mixture ModelsInternational Conference on Machine Learning (ICML), 2023 Jamil Arbas H. Ashtiani Christopher Liaw 273 31 0 07 Mar 2023
On the Privacy-Robustness-Utility Trilemma in Distributed LearningInternational Conference on Machine Learning (ICML), 2023 Youssef Allouah R. Guerraoui Nirupam Gupta Rafael Pinot John Stephan FedML 193 31 0 09 Feb 2023
On Private and Robust BanditsNeural Information Processing Systems (NeurIPS), 2023 Yulian Wu Xingyu Zhou Youming Tao Haiyan Zhao 281 9 0 06 Feb 2023
From Robustness to Privacy and BackInternational Conference on Machine Learning (ICML), 2023 Hilal Asi Jonathan R. Ullman Lydia Zakynthinou 214 37 0 03 Feb 2023
Near Optimal Private and Robust Linear Regression Xiyang Liu Prateek Jain Weihao Kong Sewoong Oh A. Suggala 211 10 0 30 Jan 2023
Fast, Sample-Efficient, Affine-Invariant Private Mean and Covariance Estimation for Subgaussian DistributionsAnnual Conference Computational Learning Theory (COLT), 2023 Gavin Brown Samuel B. Hopkins Adam D. Smith FedML 318 22 0 28 Jan 2023
A Fast Algorithm for Adaptive Private Mean Estimation John C. Duchi Saminul Haque Rohith Kuditipudi FedML 159 16 0 17 Jan 2023
Privately Estimating a Gaussian: Efficient, Robust and Optimal Daniel Alabi Pravesh Kothari Pranay Tankala Prayaag Venkat Fred Zhang 346 0 0 15 Dec 2022
Analyzing the Differentially Private Theil-Sen Estimator for Simple Linear RegressionProceedings on Privacy Enhancing Technologies (PoPETs), 2022 Jayshree Sarathy Salil P. Vadhan 271 8 0 27 Jul 2022
Differentially Private Sampling from Rashomon Sets, and the Universality of Langevin Diffusion for Convex Optimization Arun Ganesh Abhradeep Thakurta Jalaj Upadhyay 358 1 0 04 Apr 2022