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"

50 / 73 papers shown

Title
Second-Order Convergence in Private Stochastic Non-Convex Optimization Youming Tao Zuyuan Zhang Dongxiao Yu Xiuzhen Cheng Falko Dressler Di Wang 12 0 0 21 May 2025
Dyn-D $^2$ P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee Zehan Zhu Yan Huang Xin Wang Shouling Ji Jinming Xu 31 0 0 10 May 2025
Towards User-level Private Reinforcement Learning with Human Feedback Jingyang Zhang Mingxi Lei Meng Ding Mengdi Li Zihang Xiang Difei Xu Jinhui Xu Di Wang 52 0 0 22 Feb 2025
GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models Jiadong Lou Xu Yuan Rui Zhang Xingliang Yuan Neil Gong N. Tzeng AAML 50 1 0 19 Jan 2025
Differentially Private Bilevel Optimization Guy Kornowski 222 0 0 29 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 50 1 0 19 Aug 2024
Private Zeroth-Order Nonsmooth Nonconvex Optimization Qinzi Zhang Hoang Tran Ashok Cutkosky 42 4 0 27 Jun 2024
PrivSGP-VR: Differentially Private Variance-Reduced Stochastic Gradient Push with Tight Utility Bounds Zehan Zhu Yan Huang Xin Wang Jinming Xu 51 0 0 04 May 2024
Privacy of SGD under Gaussian or Heavy-Tailed Noise: Guarantees without Gradient Clipping Umut Simsekli Mert Gurbuzbalaban S. Yıldırım Lingjiong Zhu 43 2 0 04 Mar 2024
Differentially Private Non-convex Learning for Multi-layer Neural Networks Hanpu Shen Cheng-Long Wang Zihang Xiang Yiming Ying Di Wang 54 7 0 12 Oct 2023
The Relative Gaussian Mechanism and its Application to Private Gradient Descent Hadrien Hendrikx Paul Mangold A. Bellet 38 1 0 29 Aug 2023
Convergence and Privacy of Decentralized Nonconvex Optimization with Gradient Clipping and Communication Compression Boyue Li Yuejie Chi 26 12 0 17 May 2023
Differentially Private Stochastic Convex Optimization in (Non)-Euclidean Space Revisited Jinyan Su Changhong Zhao Di Wang 35 4 0 31 Mar 2023
Differentially Private Synthetic Control Saeyoung Rho Rachel Cummings V. Misra 16 2 0 24 Mar 2023
Differentially Private Diffusion Models Generate Useful Synthetic Images Sahra Ghalebikesabi Leonard Berrada Sven Gowal Ira Ktena Robert Stanforth Jamie Hayes Soham De Samuel L. Smith Olivia Wiles Borja Balle DiffM 40 69 0 27 Feb 2023
From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning Edwige Cyffers A. Bellet D. Basu FedML 36 5 0 24 Feb 2023
Private (Stochastic) Non-Convex Optimization Revisited: Second-Order Stationary Points and Excess Risks Arun Ganesh Daogao Liu Sewoong Oh Abhradeep Thakurta ODL 34 13 0 20 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 22 7 0 13 Feb 2023
Differentially Private Optimization for Smooth Nonconvex ERM Changyu Gao Stephen J. Wright 16 6 0 09 Feb 2023
Private estimation algorithms for stochastic block models and mixture models Hongjie Chen Vincent Cohen-Addad Tommaso dÓrsi Alessandro Epasto Jacob Imola David Steurer Stefan Tiegel FedML 45 20 0 11 Jan 2023
Differentially Private Learning with Per-Sample Adaptive Clipping Tianyu Xia Shuheng Shen Su Yao Xinyi Fu Ke Xu Xiaolong Xu Xingbo Fu 40 16 0 01 Dec 2022
Distributed DP-Helmet: Scalable Differentially Private Non-interactive Averaging of Single Layers Moritz Kirschte Sebastian Meiser Saman Ardalan Esfandiar Mohammadi FedML 34 0 0 03 Nov 2022
Privacy-preserving Non-negative Matrix Factorization with Outliers Swapnil Saha H. Imtiaz PICV 21 3 0 02 Nov 2022
Private Isotonic Regression Badih Ghazi Pritish Kamath Ravi Kumar Pasin Manurangsi 27 0 0 27 Oct 2022
DPIS: An Enhanced Mechanism for Differentially Private SGD with Importance Sampling Jianxin Wei Ergute Bao X. Xiao Yifan Yang 48 20 0 18 Oct 2022
Momentum Aggregation for Private Non-convex ERM Hoang Tran Ashok Cutkosky 31 14 0 12 Oct 2022
Differentially Private Deep Learning with ModelMix Hanshen Xiao Jun Wan S. Devadas 29 3 0 07 Oct 2022
Optimizing the Performative Risk under Weak Convexity Assumptions Yulai Zhao 38 5 0 02 Sep 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 43 58 0 01 Jul 2022
Beyond Uniform Lipschitz Condition in Differentially Private Optimization Rudrajit Das Satyen Kale Zheng Xu Tong Zhang Sujay Sanghavi 31 17 0 21 Jun 2022
Theoretical Error Performance Analysis for Variational Quantum Circuit Based Functional Regression Jun Qi Chao-Han Huck Yang Pin-Yu Chen Min-hsiu Hsieh 41 50 0 08 Jun 2022
Sharper Utility Bounds for Differentially Private Models Yilin Kang Yong Liu Jian Li Weiping Wang FedML 35 3 0 22 Apr 2022
No Free Lunch Theorem for Security and Utility in Federated Learning Xiaojin Zhang Hanlin Gu Lixin Fan Kai Chen Qiang Yang FedML 24 64 0 11 Mar 2022
Continual and Sliding Window Release for Private Empirical Risk Minimization Lauren Watson Abhirup Ghosh Benedek Rozemberczki Rik Sarkar 27 0 0 07 Mar 2022
Differentially Private Regression with Unbounded Covariates Jason Milionis Alkis Kalavasis Dimitris Fotakis Stratis Ioannidis 23 10 0 19 Feb 2022
Differentially Private Temporal Difference Learning with Stochastic Nonconvex-Strongly-Concave Optimization Canzhe Zhao Yanjie Ze Jing Dong Baoxiang Wang Shuai Li 54 4 0 25 Jan 2022
Public Data-Assisted Mirror Descent for Private Model Training Ehsan Amid Arun Ganesh Rajiv Mathews Swaroop Indra Ramaswamy Shuang Song Thomas Steinke Vinith Suriyakumar Om Thakkar Abhradeep Thakurta 21 49 0 01 Dec 2021
Differentially Private Coordinate Descent for Composite Empirical Risk Minimization Paul Mangold A. Bellet Joseph Salmon Marc Tommasi 37 14 0 22 Oct 2021
Adaptive Differentially Private Empirical Risk Minimization Xiaoxia Wu Lingxiao Wang Irina Cristali Quanquan Gu Rebecca Willett 43 6 0 14 Oct 2021
One-Bit Matrix Completion with Differential Privacy Zhengpin Li Zheng Wei Zengfeng Huang Xiaojun Mao Jian Wang 27 0 0 02 Oct 2021
A Fairness Analysis on Private Aggregation of Teacher Ensembles Cuong Tran M. H. Dinh Kyle Beiter Ferdinando Fioretto 21 12 0 17 Sep 2021
Differentially Private Stochastic Optimization: New Results in Convex and Non-Convex Settings Raef Bassily Cristóbal Guzmán Michael Menart 52 55 0 12 Jul 2021
Survey: Leakage and Privacy at Inference Time Marija Jegorova Chaitanya Kaul Charlie Mayor Alison Q. OÑeil Alexander Weir Roderick Murray-Smith Sotirios A. Tsaftaris PILM MIACV 33 71 0 04 Jul 2021
The Power of Sampling: Dimension-free Risk Bounds in Private ERM Yin Tat Lee Daogao Liu Zhou Lu 27 3 0 28 May 2021
Differentially private inference via noisy optimization Marco Avella-Medina Casey Bradshaw Po-Ling Loh FedML 45 29 0 19 Mar 2021
Non-Euclidean Differentially Private Stochastic Convex Optimization: Optimal Rates in Linear Time Raef Bassily Cristóbal Guzmán Anupama Nandi 59 66 0 01 Mar 2021
Do Not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning Da Yu Huishuai Zhang Wei Chen Tie-Yan Liu FedML SILM 94 111 0 25 Feb 2021
Deep Learning with Label Differential Privacy Badih Ghazi Noah Golowich Ravi Kumar Pasin Manurangsi Chiyuan Zhang 51 147 0 11 Feb 2021
Differentially Private ADMM Algorithms for Machine Learning Tao Xu Fanhua Shang Yuanyuan Liu Hongying Liu Longjie Shen Maoguo Gong 38 17 0 31 Oct 2020
On Differentially Private Stochastic Convex Optimization with Heavy-tailed Data Di Wang Hanshen Xiao S. Devadas Jinhui Xu 32 56 0 21 Oct 2020