Evading Curse of Dimensionality in Unconstrained Private GLMs via Private Gradient Descent

11 June 2020

Papers citing "Evading Curse of Dimensionality in Unconstrained Private GLMs via Private Gradient Descent"

8 / 8 papers shown

Title
DC-SGD: Differentially Private SGD with Dynamic Clipping through Gradient Norm Distribution Estimation Chengkun Wei Weixian Li Chen Gong Wenzhi Chen 55 0 0 29 Mar 2025
PILLAR: How to make semi-private learning more effective Francesco Pinto Yaxian Hu Fanny Yang Amartya Sanyal 40 11 0 06 Jun 2023
Near Optimal Private and Robust Linear Regression Xiyang Liu Prateek Jain Weihao Kong Sewoong Oh A. Suggala 33 9 0 30 Jan 2023
Differentially Private Image Classification from Features Harsh Mehta Walid Krichene Abhradeep Thakurta Alexey Kurakin Ashok Cutkosky 46 7 0 24 Nov 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 25 58 0 01 Jul 2022
Toward Training at ImageNet Scale with Differential Privacy Alexey Kurakin Shuang Song Steve Chien Roxana Geambasu Andreas Terzis Abhradeep Thakurta 22 99 0 28 Jan 2022
Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy Xinwei Zhang Xiangyi Chen Min-Fong Hong Zhiwei Steven Wu Jinfeng Yi FedML 22 90 0 25 Jun 2021
Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging Schemes Ohad Shamir Tong Zhang 99 570 0 08 Dec 2012