Private Stochastic Convex Optimization: Optimal Rates in Linear Time

10 May 2020

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

50 / 135 papers shown

Title
Purifying Approximate Differential Privacy with Randomized Post-processing Yingyu Lin Erchi Wang Yi-An Ma Yu-Xiang Wang 44 0 0 27 Mar 2025
Characterizing the Accuracy-Communication-Privacy Trade-off in Distributed Stochastic Convex Optimization Sudeep Salgia Nikola Pavlovic Yuejie Chi Qing Zhao 49 0 0 06 Jan 2025
Faster Algorithms for User-Level Private Stochastic Convex Optimization Andrew Lowy Daogao Liu Hilal Asi 33 0 0 24 Oct 2024
Adaptive Batch Size for Privately Finding Second-Order Stationary Points Daogao Liu Kunal Talwar 192 0 0 10 Oct 2024
Noise is All You Need: Private Second-Order Convergence of Noisy SGD Dmitrii Avdiukhin Michael Dinitz Chenglin Fan G. Yaroslavtsev 34 1 0 09 Oct 2024
Improved Sample Complexity for Private Nonsmooth Nonconvex Optimization Guy Kornowski Daogao Liu Kunal Talwar 34 2 0 08 Oct 2024
Differentially Private Bilevel Optimization Guy Kornowski 201 0 0 29 Sep 2024
Federated Online Prediction from Experts with Differential Privacy: Separations and Regret Speed-ups Fengyu Gao Ruiquan Huang Jing Yang FedML 35 0 0 27 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 48 1 0 19 Aug 2024
Private and Federated Stochastic Convex Optimization: Efficient Strategies for Centralized Systems Roie Reshef Kfir Y. Levy FedML 37 0 0 17 Jul 2024
Private Heterogeneous Federated Learning Without a Trusted Server Revisited: Error-Optimal and Communication-Efficient Algorithms for Convex Losses Changyu Gao Andrew Lowy Xingyu Zhou Stephen J. Wright FedML 34 2 0 12 Jul 2024
Privacy of the last iterate in cyclically-sampled DP-SGD on nonconvex composite losses Weiwei Kong Mónica Ribero 37 3 0 07 Jul 2024
Private Zeroth-Order Nonsmooth Nonconvex Optimization Qinzi Zhang Hoang Tran Ashok Cutkosky 40 4 0 27 Jun 2024
On Convex Optimization with Semi-Sensitive Features Badih Ghazi Pritish Kamath Ravi Kumar Pasin Manurangsi Raghu Meka Chiyuan Zhang 30 0 0 27 Jun 2024
Private Geometric Median Mahdi Haghifam Thomas Steinke Jonathan R. Ullman 41 0 0 11 Jun 2024
Private Online Learning via Lazy Algorithms Hilal Asi Tomer Koren Daogao Liu Kunal Talwar 109 0 0 05 Jun 2024
Private Stochastic Convex Optimization with Heavy Tails: Near-Optimality from Simple Reductions Hilal Asi Daogao Liu Kevin Tian 48 3 0 04 Jun 2024
Learning with User-Level Local Differential Privacy Puning Zhao Li Shen Rongfei Fan Qingming Li Huiwen Wu Xiaogang Xu Zhe Liu 34 2 0 27 May 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 55 7 0 22 May 2024
Neural Collapse Meets Differential Privacy: Curious Behaviors of NoisyGD with Near-perfect Representation Learning Chendi Wang Yuqing Zhu Weijie J. Su Yu-Xiang Wang AAML 58 4 0 14 May 2024
Uniformly Stable Algorithms for Adversarial Training and Beyond Jiancong Xiao Jiawei Zhang Zhimin Luo Asuman Ozdaglar AAML 48 0 0 03 May 2024
FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneity Kai Yi Nidham Gazagnadou Peter Richtárik Lingjuan Lyu 79 11 0 15 Apr 2024
Differentially Private Worst-group Risk Minimization Xinyu Zhou Raef Bassily 41 2 0 29 Feb 2024
How to Make the Gradients Small Privately: Improved Rates for Differentially Private Non-Convex Optimization Andrew Lowy Jonathan R. Ullman Stephen J. Wright 43 6 0 17 Feb 2024
Differentially Private SGD Without Clipping Bias: An Error-Feedback Approach Xinwei Zhang Zhiqi Bu Zhiwei Steven Wu Mingyi Hong 22 7 0 24 Nov 2023
Differentially Private Non-Convex Optimization under the KL Condition with Optimal Rates Michael Menart Enayat Ullah Raman Arora Raef Bassily Cristóbal Guzmán 38 2 0 22 Nov 2023
User-level Differentially Private Stochastic Convex Optimization: Efficient Algorithms with Optimal Rates Hilal Asi Daogao Liu 32 9 0 07 Nov 2023
Optimal Guarantees for Algorithmic Reproducibility and Gradient Complexity in Convex Optimization Liang Zhang Junchi Yang Amin Karbasi Niao He 34 2 0 26 Oct 2023
Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy Yingyu Lin Yian Ma Yu-Xiang Wang Rachel Redberg Zhiqi Bu 36 4 0 23 Oct 2023
DPZero: Private Fine-Tuning of Language Models without Backpropagation Liang Zhang Bingcong Li K. K. Thekumparampil Sewoong Oh Niao He 28 11 0 14 Oct 2023
Differentially Private Non-convex Learning for Multi-layer Neural Networks Hanpu Shen Cheng-Long Wang Zihang Xiang Yiming Ying Di Wang 49 7 0 12 Oct 2023
Improved Analysis of Sparse Linear Regression in Local Differential Privacy Model Liyang Zhu Meng Ding Vaneet Aggarwal Jinhui Xu Di Wang 26 4 0 11 Oct 2023
Tight Bounds for Machine Unlearning via Differential Privacy Yiyang Huang C. Canonne MU 25 11 0 02 Sep 2023
The Relative Gaussian Mechanism and its Application to Private Gradient Descent Hadrien Hendrikx Paul Mangold A. Bellet 33 1 0 29 Aug 2023
The importance of feature preprocessing for differentially private linear optimization Ziteng Sun A. Suresh A. Menon 30 3 0 19 Jul 2023
Safeguarding Data in Multimodal AI: A Differentially Private Approach to CLIP Training Alyssa Huang Peihan Liu Ryumei Nakada Linjun Zhang Wanrong Zhang VLM 76 5 0 13 Jun 2023
(Amplified) Banded Matrix Factorization: A unified approach to private training Christopher A. Choquette-Choo Arun Ganesh Ryan McKenna H. B. McMahan Keith Rush Abhradeep Thakurta Zheng Xu FedML 33 36 0 13 Jun 2023
Learning across Data Owners with Joint Differential Privacy Yangsibo Huang Haotian Jiang Daogao Liu Mohammad Mahdian Jieming Mao Vahab Mirrokni FedML 47 0 0 25 May 2023
Privacy Loss of Noisy Stochastic Gradient Descent Might Converge Even for Non-Convex Losses S. Asoodeh Mario Díaz 20 6 0 17 May 2023
Convergence and Privacy of Decentralized Nonconvex Optimization with Gradient Clipping and Communication Compression Boyue Li Yuejie Chi 26 12 0 17 May 2023
On User-Level Private Convex Optimization Badih Ghazi Pritish Kamath Ravi Kumar Raghu Meka Pasin Manurangsi Chiyuan Zhang FedML 8 8 0 08 May 2023
Differentially Private Stochastic Convex Optimization in (Non)-Euclidean Space Revisited Jinyan Su Changhong Zhao Di Wang 33 4 0 31 Mar 2023
Near-Optimal Algorithms for Private Online Optimization in the Realizable Regime Hilal Asi Vitaly Feldman Tomer Koren Kunal Talwar 42 9 0 27 Feb 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
Faster high-accuracy log-concave sampling via algorithmic warm starts Jason M. Altschuler Sinho Chewi 29 35 0 20 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 13 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 26 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
Near Optimal Private and Robust Linear Regression Xiyang Liu Prateek Jain Weihao Kong Sewoong Oh A. Suggala 41 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 28 12 0 01 Jan 2023