Beyond Uniform Lipschitz Condition in Differentially Private
Optimization

Beyond Uniform Lipschitz Condition in Differentially Private Optimization

21 June 2022

Tong Zhang

Sujay Sanghavi

Papers citing "Beyond Uniform Lipschitz Condition in Differentially Private Optimization"

16 / 16 papers shown

Title
Scaled Supervision is an Implicit Lipschitz Regularizer Z. Ouyang Chunhui Zhang Yaning Jia Soroush Vosoughi BDL OffRL 72 0 0 19 Mar 2025
Differentially Private Federated Learning With Time-Adaptive Privacy Spending Shahrzad Kiani Nupur Kulkarni Adam Dziedzic S. Draper Franziska Boenisch FedML Presented at ResearchTrend Connect \| FedML on 28 Mar 2025 136 0 0 25 Feb 2025
Data Deletion for Linear Regression with Noisy SGD Zhangjie Xia Chi-Hua Wang Guang Cheng 25 2 0 12 Oct 2024
Differential Private Stochastic Optimization with Heavy-tailed Data: Towards Optimal Rates Puning Zhao Jiafei Wu Zhe Liu Chong Wang Rongfei Fan Qingming Li 40 1 0 19 Aug 2024
BadGD: A unified data-centric framework to identify gradient descent vulnerabilities ChiHua Wang Guang Cheng SILM 37 5 0 24 May 2024
Convergence and Privacy of Decentralized Nonconvex Optimization with Gradient Clipping and Communication Compression Boyue Li Yuejie Chi 21 12 0 17 May 2023
Revisiting Gradient Clipping: Stochastic bias and tight convergence guarantees Anastasia Koloskova Hadrien Hendrikx Sebastian U. Stich 104 48 0 02 May 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
DIFF2: Differential Private Optimization via Gradient Differences for Nonconvex Distributed Learning Tomoya Murata Taiji Suzuki 8 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
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
Adaptive Differentially Private Empirical Risk Minimization Xiaoxia Wu Lingxiao Wang Irina Cristali Quanquan Gu Rebecca Willett 13 6 0 14 Oct 2021
Hyperparameter Tuning with Renyi Differential Privacy Nicolas Papernot Thomas Steinke 123 119 0 07 Oct 2021
Opacus: User-Friendly Differential Privacy Library in PyTorch Ashkan Yousefpour I. Shilov Alexandre Sablayrolles Davide Testuggine Karthik Prasad ... Sayan Gosh Akash Bharadwaj Jessica Zhao Graham Cormode Ilya Mironov VLM 144 347 0 25 Sep 2021
Differentially Private Stochastic Optimization: New Results in Convex and Non-Convex Settings Raef Bassily Cristóbal Guzmán Michael Menart 39 54 0 12 Jul 2021
Tempered Sigmoid Activations for Deep Learning with Differential Privacy Nicolas Papernot Abhradeep Thakurta Shuang Song Steve Chien Ulfar Erlingsson AAML 128 178 0 28 Jul 2020