Differentially Private Empirical Risk Minimization Revisited: Faster and More General

14 February 2018

Papers citing "Differentially Private Empirical Risk Minimization Revisited: Faster and More General"

23 / 73 papers shown

Title
On the Intrinsic Differential Privacy of Bagging Hongbin Liu Jinyuan Jia Neil Zhenqiang Gong FedML SILM 79 8 0 22 Aug 2020
Stochastic Adaptive Line Search for Differentially Private Optimization Chen Chen Jaewoo Lee 24 14 0 18 Aug 2020
More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence Tianqing Zhu Dayong Ye Wei Wang Wanlei Zhou Philip S. Yu SyDa 38 125 0 05 Aug 2020
The Trade-Offs of Private Prediction Laurens van der Maaten Awni Y. Hannun 25 22 0 09 Jul 2020
Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification Yingxue Zhou Zhiwei Steven Wu A. Banerjee 29 108 0 07 Jul 2020
Private Stochastic Non-Convex Optimization: Adaptive Algorithms and Tighter Generalization Bounds Yingxue Zhou Xiangyi Chen Mingyi Hong Zhiwei Steven Wu A. Banerjee 24 25 0 24 Jun 2020
Private Stochastic Convex Optimization: Optimal Rates in Linear Time Vitaly Feldman Tomer Koren Kunal Talwar 22 204 0 10 May 2020
Data Heterogeneity Differential Privacy: From Theory to Algorithm Yilin Kang Jian Li Yong Liu Weiping Wang 36 1 0 20 Feb 2020
Input Perturbation: A New Paradigm between Central and Local Differential Privacy Yilin Kang Yong Liu Ben Niu Xin-Yi Tong Likun Zhang Weiping Wang 29 11 0 20 Feb 2020
Improved Differentially Private Decentralized Source Separation for fMRI Data H. Imtiaz Jafar Mohammadi Rogers F. Silva Bradley T. Baker Sergey Plis Anand D. Sarwate Vince D. Calhoun OOD 23 5 0 28 Oct 2019
Private Stochastic Convex Optimization with Optimal Rates Raef Bassily Vitaly Feldman Kunal Talwar Abhradeep Thakurta 23 237 0 27 Aug 2019
DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERM Bao Wang Quanquan Gu M. Boedihardjo Farzin Barekat Stanley J. Osher 22 25 0 28 Jun 2019
Data-Dependent Differentially Private Parameter Learning for Directed Graphical Models Amrita Roy Chowdhury Theodoros Rekatsinas S. Jha 20 10 0 30 May 2019
Differentially Private Empirical Risk Minimization with Sparsity-Inducing Norms K. S. S. Kumar M. Deisenroth 19 6 0 13 May 2019
Evaluating Differentially Private Machine Learning in Practice Bargav Jayaraman David Evans 15 7 0 24 Feb 2019
DP-ADMM: ADMM-based Distributed Learning with Differential Privacy Zonghao Huang Rui Hu Yuanxiong Guo Eric Chan-Tin Yanmin Gong FedML 11 194 0 30 Aug 2018
Concentrated Differentially Private Gradient Descent with Adaptive per-Iteration Privacy Budget Jaewoo Lee Daniel Kifer 22 156 0 28 Aug 2018
Differentially Private Confidence Intervals for Empirical Risk Minimization Yue Wang Daniel Kifer Jaewoo Lee 27 34 0 11 Apr 2018
Privacy-preserving Prediction Cynthia Dwork Vitaly Feldman 25 90 0 27 Mar 2018
Empirical Risk Minimization in Non-interactive Local Differential Privacy: Efficiency and High Dimensional Case Di Wang Marco Gaboardi Jinhui Xu 26 61 0 12 Feb 2018
Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition Hamed Karimi J. Nutini Mark Schmidt 139 1,205 0 16 Aug 2016
Calculus of the exponent of Kurdyka-Łojasiewicz inequality and its applications to linear convergence of first-order methods Guoyin Li Ting Kei Pong 105 292 0 09 Feb 2016
A Proximal Stochastic Gradient Method with Progressive Variance Reduction Lin Xiao Tong Zhang ODL 93 737 0 19 Mar 2014