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Private Mean Estimation of Heavy-Tailed Distributions

21 February 2020

Papers citing "Private Mean Estimation of Heavy-Tailed Distributions"

50 / 75 papers shown

Title
Differentially Private Sparse Linear Regression with Heavy-tailed Responses Xizhi Tian Meng Ding Touming Tao Zihang Xiang Di Wang 18 0 0 07 Jun 2025
Towards hyperparameter-free optimization with differential privacy Zhiqi Bu Ruixuan Liu 82 2 0 02 Mar 2025
Learning with Differentially Private (Sliced) Wasserstein Gradients David Rodríguez-Vítores Clément Lalanne Jean-Michel Loubes FedML 111 0 0 03 Feb 2025
Differentially Private Multi-Sampling from Distributions Albert Cheu Debanuj Nayak 72 1 0 13 Dec 2024
Statistical-Computational Trade-offs for Density Estimation Anders Aamand Alexandr Andoni Justin Y. Chen Piotr Indyk Shyam Narayanan Sandeep Silwal Haike Xu 22 0 0 30 Oct 2024
Privately Learning Smooth Distributions on the Hypercube by Projections Clément Lalanne Sébastien Gadat 72 1 0 16 Sep 2024
Differential Private Stochastic Optimization with Heavy-tailed Data: Towards Optimal Rates Puning Zhao Xiaogang Xu Zhe Liu Chong Wang Rongfei Fan Qingming Li 72 1 0 19 Aug 2024
Private Means and the Curious Incident of the Free Lunch Jack Fitzsimons James Honaker Michael Shoemate Vikrant Singhal 85 2 0 19 Aug 2024
On Differentially Private U Statistics Kamalika Chaudhuri Po-Ling Loh Shourya Pandey Purnamrita Sarkar FedML 92 1 0 06 Jul 2024
Distribution Learnability and Robustness Shai Ben-David Alex Bie Gautam Kamath Tosca Lechner 94 2 0 25 Jun 2024
Optimal Federated Learning for Nonparametric Regression with Heterogeneous Distributed Differential Privacy Constraints T. T. Cai Abhinav Chakraborty Lasse Vuursteen FedML 83 3 0 10 Jun 2024
Federated Nonparametric Hypothesis Testing with Differential Privacy Constraints: Optimal Rates and Adaptive Tests T. T. Cai Abhinav Chakraborty Lasse Vuursteen FedML 77 2 0 10 Jun 2024
A Huber Loss Minimization Approach to Mean Estimation under User-level Differential Privacy Puning Zhao Lifeng Lai Li Shen Qingming Li Xiaogang Xu Zhe Liu 79 7 0 22 May 2024
Near-Optimal differentially private low-rank trace regression with guaranteed private initialization Mengyue Zha 67 0 0 24 Mar 2024
A Simple and Practical Method for Reducing the Disparate Impact of Differential Privacy Lucas Rosenblatt Julia Stoyanovich Christopher Musco 52 2 0 18 Dec 2023
Mean estimation in the add-remove model of differential privacy Alex Kulesza A. Suresh Yuyan Wang 56 3 0 11 Dec 2023
Sample-Optimal Locally Private Hypothesis Selection and the Provable Benefits of Interactivity A. F. Pour Hassan Ashtiani S. Asoodeh 76 1 0 09 Dec 2023
Instance-Specific Asymmetric Sensitivity in Differential Privacy David Durfee 94 1 0 02 Nov 2023
Improved Analysis of Sparse Linear Regression in Local Differential Privacy Model Liyang Zhu Meng Ding Vaneet Aggarwal Jinhui Xu Di Wang 40 5 0 11 Oct 2023
Better and Simpler Lower Bounds for Differentially Private Statistical Estimation Shyam Narayanan FedML 68 11 0 10 Oct 2023
Striking a Balance: An Optimal Mechanism Design for Heterogenous Differentially Private Data Acquisition for Logistic Regression Ameya Anjarlekar Rasoul Etesami R. Srikant 113 3 0 19 Sep 2023
Private Federated Learning with Autotuned Compression Enayat Ullah Christopher A. Choquette-Choo Peter Kairouz Sewoong Oh FedML 68 8 0 20 Jul 2023
Data Structures for Density Estimation Anders Aamand Alexandr Andoni Justin Y. Chen Piotr Indyk Shyam Narayanan Sandeep Silwal 28 5 0 20 Jun 2023
PLAN: Variance-Aware Private Mean Estimation Martin Aumüller C. Lebeda Boel Nelson Rasmus Pagh FedML 63 4 0 14 Jun 2023
Differentially Private Episodic Reinforcement Learning with Heavy-tailed Rewards Yulian Wu Xingyu Zhou Sayak Ray Chowdhury Di Wang 60 2 0 01 Jun 2023
A Polynomial Time, Pure Differentially Private Estimator for Binary Product Distributions Vikrant Singhal 91 9 0 13 Apr 2023
Score Attack: A Lower Bound Technique for Optimal Differentially Private Learning T. Tony Cai Yichen Wang Linjun Zhang 81 19 0 13 Mar 2023
Subset-Based Instance Optimality in Private Estimation Travis Dick Alex Kulesza Ziteng Sun A. Suresh 101 9 0 01 Mar 2023
On Private and Robust Bandits Yulian Wu Xingyu Zhou Youming Tao Di Wang 60 7 0 06 Feb 2023
From Robustness to Privacy and Back Hilal Asi Jonathan R. Ullman Lydia Zakynthinou 83 30 0 03 Feb 2023
Continual Mean Estimation Under User-Level Privacy Anand George Lekshmi Ramesh A. V. Singh Himanshu Tyagi FedML 65 9 0 20 Dec 2022
Privately Estimating a Gaussian: Efficient, Robust and Optimal Daniel Alabi Pravesh Kothari Pranay Tankala Prayaag Venkat Fred Zhang 123 0 0 15 Dec 2022
Robustness Implies Privacy in Statistical Estimation Samuel B. Hopkins Gautam Kamath Mahbod Majid Shyam Narayanan 102 57 0 09 Dec 2022
Privacy Induces Robustness: Information-Computation Gaps and Sparse Mean Estimation Kristian Georgiev Samuel B. Hopkins FedML 90 24 0 01 Nov 2022
Instance-Optimal Differentially Private Estimation Audra McMillan Adam D. Smith Jonathan R. Ullman 55 6 0 28 Oct 2022
Differentially private multivariate medians Kelly Ramsay Aukosh Jagannath Shojaéddin Chenouri 60 4 0 12 Oct 2022
On the Impossible Safety of Large AI Models El-Mahdi El-Mhamdi Sadegh Farhadkhani R. Guerraoui Nirupam Gupta L. Hoang Rafael Pinot Sébastien Rouault John Stephan 106 33 0 30 Sep 2022
Private Stochastic Optimization With Large Worst-Case Lipschitz Parameter: Optimal Rates for (Non-Smooth) Convex Losses and Extension to Non-Convex Losses Andrew Lowy Meisam Razaviyayn 86 13 0 15 Sep 2022
Archimedes Meets Privacy: On Privately Estimating Quantiles in High Dimensions Under Minimal Assumptions Omri Ben-Eliezer Dan Mikulincer Ilias Zadik FedML 90 7 0 15 Aug 2022
Efficient Private SCO for Heavy-Tailed Data via Clipping Chenhan Jin Kaiwen Zhou Bo Han Ming Yang James Cheng 37 1 0 27 Jun 2022
Differentially Private Maximal Information Coefficients John Lazarsfeld Aaron Johnson Emmanuel Adéníran 18 3 0 21 Jun 2022
Algorithms for bounding contribution for histogram estimation under user-level privacy Yuhan Liu A. Suresh Wennan Zhu Peter Kairouz Marco Gruteser 52 9 0 07 Jun 2022
DP-PCA: Statistically Optimal and Differentially Private PCA Xiyang Liu Weihao Kong Prateek Jain Sewoong Oh 127 24 0 27 May 2022
New Lower Bounds for Private Estimation and a Generalized Fingerprinting Lemma Gautam Kamath Argyris Mouzakis Vikrant Singhal FedML 88 30 0 17 May 2022
Private High-Dimensional Hypothesis Testing Shyam Narayanan FedML 80 12 0 03 Mar 2022
Nonparametric extensions of randomized response for private confidence sets Ian Waudby-Smith Zhiwei Steven Wu Aaditya Ramdas 81 9 0 17 Feb 2022
$Differentially Private $\ell_1$-norm Linear Regression with Heavy-tailed Data$ Differentially Private $\ell_1$ -norm Linear Regression with Heavy-tailed Data Di Wang Jinhui Xu 42 7 0 10 Jan 2022
Optimal and Differentially Private Data Acquisition: Central and Local Mechanisms Alireza Fallah A. Makhdoumi Azarakhsh Malekian Asuman Ozdaglar FedML 95 32 0 10 Jan 2022
Differentially-Private Clustering of Easy Instances E. Cohen Haim Kaplan Yishay Mansour Uri Stemmer Eliad Tsfadia 71 25 0 29 Dec 2021
Private Robust Estimation by Stabilizing Convex Relaxations Pravesh Kothari Pasin Manurangsi A. Velingker 71 47 0 07 Dec 2021