Private Stochastic Convex Optimization with Optimal Rates

27 August 2019

Papers citing "Private Stochastic Convex Optimization with Optimal Rates"

50 / 166 papers shown

Title
Characterizing the Accuracy-Communication-Privacy Trade-off in Distributed Stochastic Convex Optimization Sudeep Salgia Nikola Pavlovic Yuejie Chi Qing Zhao 39 0 0 06 Jan 2025
Faster Algorithms for User-Level Private Stochastic Convex Optimization Andrew Lowy Daogao Liu Hilal Asi 28 0 0 24 Oct 2024
Adaptive Batch Size for Privately Finding Second-Order Stationary Points Daogao Liu Kunal Talwar 130 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 32 0 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 142 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 Jiafei Wu Zhe Liu Chong Wang Rongfei Fan Qingming Li 45 1 0 19 Aug 2024
Private and Federated Stochastic Convex Optimization: Efficient Strategies for Centralized Systems Roie Reshef Kfir Y. Levy FedML 27 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 31 2 0 12 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 25 0 0 27 Jun 2024
Tangent differential privacy Lexing Ying 16 0 0 06 Jun 2024
Private Online Learning via Lazy Algorithms Hilal Asi Tomer Koren Daogao Liu Kunal Talwar 104 0 0 05 Jun 2024
Private Stochastic Convex Optimization with Heavy Tails: Near-Optimality from Simple Reductions Hilal Asi Daogao Liu Kevin Tian 40 3 0 04 Jun 2024
Learning with User-Level Local Differential Privacy Puning Zhao Li Shen Rongfei Fan Qingming Li Huiwen Wu Jiafei Wu Zhe Liu 32 2 0 27 May 2024
BadGD: A unified data-centric framework to identify gradient descent vulnerabilities ChiHua Wang Guang Cheng SILM 40 5 0 24 May 2024
A Huber Loss Minimization Approach to Mean Estimation under User-level Differential Privacy Puning Zhao Lifeng Lai Li Shen Qingming Li Jiafei Wu Zhe Liu 47 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 50 4 0 14 May 2024
Uniformly Stable Algorithms for Adversarial Training and Beyond Jiancong Xiao Jiawei Zhang Zhimin Luo Asuman Ozdaglar AAML 45 0 0 03 May 2024
Advances in Differential Privacy and Differentially Private Machine Learning Saswat Das Subhankar Mishra 22 3 0 06 Apr 2024
Public-data Assisted Private Stochastic Optimization: Power and Limitations Enayat Ullah Michael Menart Raef Bassily Cristóbal Guzmán Raman Arora 30 1 0 06 Mar 2024
Mirror Descent Algorithms with Nearly Dimension-Independent Rates for Differentially-Private Stochastic Saddle-Point Problems Tomás González Cristóbal Guzmán Courtney Paquette 34 3 0 05 Mar 2024
Shifted Interpolation for Differential Privacy Jinho Bok Weijie Su Jason M. Altschuler 20 8 0 01 Mar 2024
Differentially Private Worst-group Risk Minimization Xinyu Zhou Raef Bassily 35 2 0 29 Feb 2024
On the Convergence of Differentially-Private Fine-tuning: To Linearly Probe or to Fully Fine-tune? Shuqi Ke Charlie Hou Giulia Fanti Sewoong Oh 38 4 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 41 6 0 17 Feb 2024
RQP-SGD: Differential Private Machine Learning through Noisy SGD and Randomized Quantization Ce Feng Parv Venkitasubramaniam 29 1 0 09 Feb 2024
Personalized Differential Privacy for Ridge Regression Krishna Acharya Franziska Boenisch Rakshit Naidu Juba Ziani 13 2 0 30 Jan 2024
Differentially Private Non-Convex Optimization under the KL Condition with Optimal Rates Michael Menart Enayat Ullah Raman Arora Raef Bassily Cristóbal Guzmán 32 2 0 22 Nov 2023
User-level Differentially Private Stochastic Convex Optimization: Efficient Algorithms with Optimal Rates Hilal Asi Daogao Liu 24 8 0 07 Nov 2023
Initialization Matters: Privacy-Utility Analysis of Overparameterized Neural Networks Jiayuan Ye Zhenyu Zhu Fanghui Liu Reza Shokri V. Cevher 32 12 0 31 Oct 2023
Differentially Private Reward Estimation with Preference Feedback Sayak Ray Chowdhury Xingyu Zhou Nagarajan Natarajan 38 4 0 30 Oct 2023
DP-SGD with weight clipping Antoine Barczewski Jan Ramon 11 1 0 27 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
Improved Analysis of Sparse Linear Regression in Local Differential Privacy Model Liyang Zhu Meng Ding Vaneet Aggarwal Jinhui Xu Di Wang 23 4 0 11 Oct 2023
Better and Simpler Lower Bounds for Differentially Private Statistical Estimation Shyam Narayanan FedML 19 9 0 10 Oct 2023
Stability and Generalization for Minibatch SGD and Local SGD Yunwen Lei Tao Sun Mingrui Liu 32 3 0 02 Oct 2023
Tight Bounds for Machine Unlearning via Differential Privacy Yiyang Huang C. Canonne MU 17 10 0 02 Sep 2023
The Relative Gaussian Mechanism and its Application to Private Gradient Descent Hadrien Hendrikx Paul Mangold A. Bellet 28 1 0 29 Aug 2023
Private Federated Learning with Autotuned Compression Enayat Ullah Christopher A. Choquette-Choo Peter Kairouz Sewoong Oh FedML 15 6 0 20 Jul 2023
The importance of feature preprocessing for differentially private linear optimization Ziteng Sun A. Suresh A. Menon 22 3 0 19 Jul 2023
Differentially Private Domain Adaptation with Theoretical Guarantees Raef Bassily Corinna Cortes Anqi Mao M. Mohri 30 0 0 15 Jun 2023
Safeguarding Data in Multimodal AI: A Differentially Private Approach to CLIP Training Alyssa Huang Peihan Liu Ryumei Nakada Linjun Zhang Wanrong Zhang VLM 71 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 25 35 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 39 0 0 25 May 2023
Faster Differentially Private Convex Optimization via Second-Order Methods Arun Ganesh Mahdi Haghifam Thomas Steinke Abhradeep Thakurta 11 10 0 22 May 2023
Online Learning Under A Separable Stochastic Approximation Framework Min Gan Xiang-Xiang Su Guang-yong Chen Jing Chen 23 0 0 12 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
Revisiting Gradient Clipping: Stochastic bias and tight convergence guarantees Anastasia Koloskova Hadrien Hendrikx Sebastian U. Stich 104 49 0 02 May 2023