Private Stochastic Convex Optimization with Optimal Rates

27 August 2019

Papers citing "Private Stochastic Convex Optimization with Optimal Rates"

50 / 166 papers shown

Title
Differentially Private Stochastic Convex Optimization in (Non)-Euclidean Space Revisited Jinyan Su Changhong Zhao Di Wang 24 3 0 31 Mar 2023
Lower Generalization Bounds for GD and SGD in Smooth Stochastic Convex Optimization Peiyuan Zhang Jiaye Teng Ji Zhang 39 4 0 19 Mar 2023
Score Attack: A Lower Bound Technique for Optimal Differentially Private Learning T. Tony Cai Yichen Wang Linjun Zhang 46 16 0 13 Mar 2023
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy Natalia Ponomareva Hussein Hazimeh Alexey Kurakin Zheng Xu Carson E. Denison H. B. McMahan Sergei Vassilvitskii Steve Chien Abhradeep Thakurta 94 167 0 01 Mar 2023
Differentially Private Algorithms for the Stochastic Saddle Point Problem with Optimal Rates for the Strong Gap Raef Bassily Cristóbal Guzmán Michael Menart FedML 37 4 0 24 Feb 2023
Private (Stochastic) Non-Convex Optimization Revisited: Second-Order Stationary Points and Excess Risks Arun Ganesh Daogao Liu Sewoong Oh Abhradeep Thakurta ODL 27 12 0 20 Feb 2023
Why Is Public Pretraining Necessary for Private Model Training? Arun Ganesh Mahdi Haghifam Milad Nasr Sewoong Oh Thomas Steinke Om Thakkar Abhradeep Thakurta Lun Wang 23 36 0 19 Feb 2023
Algorithmic Aspects of the Log-Laplace Transform and a Non-Euclidean Proximal Sampler Sivakanth Gopi Y. Lee Daogao Liu Ruoqi Shen Kevin Tian 17 7 0 13 Feb 2023
DIFF2: Differential Private Optimization via Gradient Differences for Nonconvex Distributed Learning Tomoya Murata Taiji Suzuki 22 8 0 08 Feb 2023
Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy Anastasia Koloskova Ryan McKenna Zachary B. Charles Keith Rush Brendan McMahan 27 8 0 02 Feb 2023
Near Optimal Private and Robust Linear Regression Xiyang Liu Prateek Jain Weihao Kong Sewoong Oh A. Suggala 38 9 0 30 Jan 2023
ReSQueing Parallel and Private Stochastic Convex Optimization Y. Carmon A. Jambulapati Yujia Jin Y. Lee Daogao Liu Aaron Sidford Kevin Tian FedML 22 12 0 01 Jan 2023
Reconstructing Training Data from Model Gradient, Provably Zihan Wang Jason D. Lee Qi Lei FedML 27 24 0 07 Dec 2022
Differentially Private Image Classification from Features Harsh Mehta Walid Krichene Abhradeep Thakurta Alexey Kurakin Ashok Cutkosky 52 7 0 24 Nov 2022
Distributed DP-Helmet: Scalable Differentially Private Non-interactive Averaging of Single Layers Moritz Kirschte Sebastian Meiser Saman Ardalan Esfandiar Mohammadi FedML 34 0 0 03 Nov 2022
Private optimization in the interpolation regime: faster rates and hardness results Hilal Asi Karan N. Chadha Gary Cheng John C. Duchi 47 5 0 31 Oct 2022
DPVIm: Differentially Private Variational Inference Improved Joonas Jälkö Lukas Prediger Antti Honkela Samuel Kaski 31 3 0 28 Oct 2022
Private Online Prediction from Experts: Separations and Faster Rates Hilal Asi Vitaly Feldman Tomer Koren Kunal Talwar FedML 32 18 0 24 Oct 2022
Stochastic Differentially Private and Fair Learning Andrew Lowy Devansh Gupta Meisam Razaviyayn FaML FedML 16 13 0 17 Oct 2022
Differentially Private Online-to-Batch for Smooth Losses Qinzi Zhang Hoang Tran Ashok Cutkosky FedML 46 4 0 12 Oct 2022
Momentum Aggregation for Private Non-convex ERM Hoang Tran Ashok Cutkosky 26 14 0 12 Oct 2022
Algorithms that Approximate Data Removal: New Results and Limitations Vinith M. Suriyakumar Ashia C. Wilson MU 44 27 0 25 Sep 2022
Stability and Generalization for Markov Chain Stochastic Gradient Methods Puyu Wang Yunwen Lei Yiming Ying Ding-Xuan Zhou 16 18 0 16 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 30 13 0 15 Sep 2022
Differentially Private Stochastic Gradient Descent with Low-Noise Puyu Wang Yunwen Lei Yiming Ying Ding-Xuan Zhou FedML 43 5 0 09 Sep 2022
Easy Differentially Private Linear Regression Kareem Amin Matthew Joseph Mónica Ribero Sergei Vassilvitskii FedML 23 16 0 15 Aug 2022
Private Convex Optimization in General Norms Sivakanth Gopi Y. Lee Daogao Liu Ruoqi Shen Kevin Tian 13 14 0 18 Jul 2022
Uniform Stability for First-Order Empirical Risk Minimization Amit Attia Tomer Koren 20 5 0 17 Jul 2022
Differentially Private Linear Bandits with Partial Distributed Feedback Fengjiao Li Xingyu Zhou Bo Ji FedML 31 13 0 12 Jul 2022
(Nearly) Optimal Private Linear Regression via Adaptive Clipping Prateeksha Varshney Abhradeep Thakurta Prateek Jain 38 8 0 11 Jul 2022
High-Dimensional Private Empirical Risk Minimization by Greedy Coordinate Descent Paul Mangold A. Bellet Joseph Salmon Marc Tommasi 35 5 0 04 Jul 2022
When Does Differentially Private Learning Not Suffer in High Dimensions? Xuechen Li Daogao Liu Tatsunori Hashimoto Huseyin A. Inan Janardhan Kulkarni Y. Lee Abhradeep Thakurta 30 58 0 01 Jul 2022
Efficient Private SCO for Heavy-Tailed Data via Clipping Chenhan Jin Kaiwen Zhou Bo Han Ming Yang James Cheng 10 1 0 27 Jun 2022
Beyond Uniform Lipschitz Condition in Differentially Private Optimization Rudrajit Das Satyen Kale Zheng Xu Tong Zhang Sujay Sanghavi 24 17 0 21 Jun 2022
$On Private Online Convex Optimization: Optimal Algorithms in $\ell_p$-Geometry and High Dimensional Contextual Bandits$ On Private Online Convex Optimization: Optimal Algorithms in $\ell_p$ -Geometry and High Dimensional Contextual Bandits Yuxuan Han Zhicong Liang Zhipeng Liang Yang Wang Yuan Yao Jiheng Zhang 28 1 0 16 Jun 2022
Differentially Private Multi-Party Data Release for Linear Regression Ruihan Wu Xin Yang Yuanshun Yao Jiankai Sun Tianyi Liu Kilian Q. Weinberger C. Wang 20 2 0 16 Jun 2022
Boosting the Confidence of Generalization for $L_2$ -Stable Randomized Learning Algorithms Xiao-Tong Yuan Ping Li 20 4 0 08 Jun 2022
Subject Granular Differential Privacy in Federated Learning Virendra J. Marathe Pallika H. Kanani Daniel W. Peterson Guy Steele Jr FedML 11 9 0 07 Jun 2022
Dimension Independent Generalization of DP-SGD for Overparameterized Smooth Convex Optimization Yi-An Ma T. V. Marinov Tong Zhang 17 8 0 03 Jun 2022
Faster Rates of Convergence to Stationary Points in Differentially Private Optimization R. Arora Raef Bassily Tomás González Cristóbal Guzmán Michael Menart Enayat Ullah 26 29 0 02 Jun 2022
Bring Your Own Algorithm for Optimal Differentially Private Stochastic Minimax Optimization Liang Zhang K. K. Thekumparampil Sewoong Oh Niao He 36 17 0 01 Jun 2022
Privacy of Noisy Stochastic Gradient Descent: More Iterations without More Privacy Loss Jason M. Altschuler Kunal Talwar FedML 33 57 0 27 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 Riemannian optimization Andi Han Bamdev Mishra Pratik Jawanpuria Junbin Gao 25 10 0 19 May 2022
Differentially Private Generalized Linear Models Revisited R. Arora Raef Bassily Cristóbal Guzmán Michael Menart Enayat Ullah FedML 20 16 0 06 May 2022
Sharper Utility Bounds for Differentially Private Models Yilin Kang Yong Liu Jian Li Weiping Wang FedML 29 3 0 22 Apr 2022
Differentially Private Learning with Margin Guarantees Raef Bassily M. Mohri A. Suresh 20 9 0 21 Apr 2022
Stability and Generalization of Differentially Private Minimax Problems Yilin Kang Yong Liu Jian Li Weiping Wang 24 3 0 11 Apr 2022
Differentially Private Sampling from Rashomon Sets, and the Universality of Langevin Diffusion for Convex Optimization Arun Ganesh Abhradeep Thakurta Jalaj Upadhyay 26 1 0 04 Apr 2022
Stochastic and Private Nonconvex Outlier-Robust PCA Tyler Maunu Chenyun Yu Gilad Lerman 11 3 0 17 Mar 2022