Private Stochastic Convex Optimization: Optimal Rates in Linear Time

10 May 2020

Papers citing "Private Stochastic Convex Optimization: Optimal Rates in Linear Time"

50 / 136 papers shown

Title
ReSQueing Parallel and Private Stochastic Convex Optimization Y. Carmon A. Jambulapati Yujia Jin Y. Lee Daogao Liu Aaron Sidford Kevin Tian FedML 28 12 0 01 Jan 2023
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 49 5 0 31 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
Resolving the Mixing Time of the Langevin Algorithm to its Stationary Distribution for Log-Concave Sampling Jason M. Altschuler Kunal Talwar 38 24 0 16 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 28 14 0 12 Oct 2022
PAC Privacy: Automatic Privacy Measurement and Control of Data Processing Hanshen Xiao S. Devadas 29 11 0 07 Oct 2022
Stability and Generalization for Markov Chain Stochastic Gradient Methods Puyu Wang Yunwen Lei Yiming Ying Ding-Xuan Zhou 24 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 51 5 0 09 Sep 2022
Private Convex Optimization in General Norms Sivakanth Gopi Y. Lee Daogao Liu Ruoqi Shen Kevin Tian 21 14 0 18 Jul 2022
Uniform Stability for First-Order Empirical Risk Minimization Amit Attia Tomer Koren 20 5 0 17 Jul 2022
(Nearly) Optimal Private Linear Regression via Adaptive Clipping Prateeksha Varshney Abhradeep Thakurta Prateek Jain 43 8 0 11 Jul 2022
High-Dimensional Private Empirical Risk Minimization by Greedy Coordinate Descent Paul Mangold A. Bellet Joseph Salmon Marc Tommasi 51 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 36 58 0 01 Jul 2022
Efficient Private SCO for Heavy-Tailed Data via Clipping Chenhan Jin Kaiwen Zhou Bo Han Ming Yang James Cheng 15 1 0 27 Jun 2022
Beyond Uniform Lipschitz Condition in Differentially Private Optimization Rudrajit Das Satyen Kale Zheng Xu Tong Zhang Sujay Sanghavi 28 17 0 21 Jun 2022
SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compression Zhize Li Haoyu Zhao Boyue Li Yuejie Chi FedML 35 41 0 20 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 36 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 25 2 0 16 Jun 2022
Boosting the Confidence of Generalization for $L_2$ -Stable Randomized Learning Algorithms Xiao-Tong Yuan Ping Li 23 4 0 08 Jun 2022
Algorithms for bounding contribution for histogram estimation under user-level privacy Yuhan Liu A. Suresh Wennan Zhu Peter Kairouz Marco Gruteser 13 8 0 07 Jun 2022
Dimension Independent Generalization of DP-SGD for Overparameterized Smooth Convex Optimization Yi Ma T. V. Marinov Tong Zhang 25 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 34 30 0 02 Jun 2022
Bring Your Own Algorithm for Optimal Differentially Private Stochastic Minimax Optimization Liang Zhang K. K. Thekumparampil Sewoong Oh Niao He 44 17 0 01 Jun 2022
Privacy of Noisy Stochastic Gradient Descent: More Iterations without More Privacy Loss Jason M. Altschuler Kunal Talwar FedML 39 57 0 27 May 2022
DP-PCA: Statistically Optimal and Differentially Private PCA Xiyang Liu Weihao Kong Prateek Jain Sewoong Oh 40 21 0 27 May 2022
Differentially private Riemannian optimization Andi Han Bamdev Mishra Pratik Jawanpuria Junbin Gao 38 10 0 19 May 2022
Differentially Private Generalized Linear Models Revisited R. Arora Raef Bassily Cristóbal Guzmán Michael Menart Enayat Ullah FedML 28 16 0 06 May 2022
Large Scale Transfer Learning for Differentially Private Image Classification Harsh Mehta Abhradeep Thakurta Alexey Kurakin Ashok Cutkosky 17 39 0 06 May 2022
Unlocking High-Accuracy Differentially Private Image Classification through Scale Soham De Leonard Berrada Jamie Hayes Samuel L. Smith Borja Balle 35 218 0 28 Apr 2022
Sharper Utility Bounds for Differentially Private Models Yilin Kang Yong Liu Jian Li Weiping Wang FedML 35 3 0 22 Apr 2022
Differentially Private Learning with Margin Guarantees Raef Bassily M. Mohri A. Suresh 28 9 0 21 Apr 2022
Stability and Generalization of Differentially Private Minimax Problems Yilin Kang Yong Liu Jian Li Weiping Wang 32 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 32 1 0 04 Apr 2022
Private Non-Convex Federated Learning Without a Trusted Server Andrew Lowy Ali Ghafelebashi Meisam Razaviyayn FedML 36 25 0 13 Mar 2022
Differentially Private Learning Needs Hidden State (Or Much Faster Convergence) Jiayuan Ye Reza Shokri FedML 35 44 0 10 Mar 2022
Private Convex Optimization via Exponential Mechanism Sivakanth Gopi Y. Lee Daogao Liu 89 52 0 01 Mar 2022
Differentially Private Regression with Unbounded Covariates Jason Milionis Alkis Kalavasis Dimitris Fotakis Stratis Ioannidis 23 10 0 19 Feb 2022
Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning A. Bietti Chen-Yu Wei Miroslav Dudík John Langford Zhiwei Steven Wu FedML 30 44 0 10 Feb 2022
Toward Training at ImageNet Scale with Differential Privacy Alexey Kurakin Shuang Song Steve Chien Roxana Geambasu Andreas Terzis Abhradeep Thakurta 36 100 0 28 Jan 2022
Differentially Private SGDA for Minimax Problems Zhenhuan Yang Shu Hu Yunwen Lei Kush R. Varshney Siwei Lyu Yiming Ying 36 19 0 22 Jan 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 8 6 0 10 Jan 2022
Simple Stochastic and Online Gradient Descent Algorithms for Pairwise Learning Zhenhuan Yang Yunwen Lei Puyu Wang Tianbao Yang Yiming Ying 11 26 0 23 Nov 2021
Differentially Private Coordinate Descent for Composite Empirical Risk Minimization Paul Mangold A. Bellet Joseph Salmon Marc Tommasi 32 14 0 22 Oct 2021
Adaptive Differentially Private Empirical Risk Minimization Xiaoxia Wu Lingxiao Wang Irina Cristali Quanquan Gu Rebecca Willett 38 6 0 14 Oct 2021
Adapting to Function Difficulty and Growth Conditions in Private Optimization Hilal Asi Daniel Levy John C. Duchi 13 23 0 05 Aug 2021
Faster Rates of Private Stochastic Convex Optimization Jinyan Su Lijie Hu Di Wang 34 12 0 31 Jul 2021
High Dimensional Differentially Private Stochastic Optimization with Heavy-tailed Data Lijie Hu Shuo Ni Hanshen Xiao Di Wang 31 52 0 23 Jul 2021