The Cost of Privacy in Generalized Linear Models: Algorithms and Minimax
Lower Bounds

The Cost of Privacy in Generalized Linear Models: Algorithms and Minimax Lower Bounds

8 November 2020

T. Tony Cai

Papers citing "The Cost of Privacy in Generalized Linear Models: Algorithms and Minimax Lower Bounds"

13 / 13 papers shown

Title
Differentially Private High Dimensional Bandits Apurv Shukla 22 0 0 06 Feb 2024
Truthful Generalized Linear Models Yuan Qiu Jinyan Liu Di Wang FedML 34 3 0 16 Sep 2022
Fast Composite Optimization and Statistical Recovery in Federated Learning Yajie Bao M. Crawshaw Sha Luo Mingrui Liu FedML 23 16 0 17 Jul 2022
Hypothesis Testing for Differentially Private Linear Regression Daniel Alabi Salil P. Vadhan FedML 35 12 0 29 Jun 2022
New Lower Bounds for Private Estimation and a Generalized Fingerprinting Lemma Gautam Kamath Argyris Mouzakis Vikrant Singhal FedML 34 26 0 17 May 2022
Differentially Private Regression with Unbounded Covariates Jason Milionis Alkis Kalavasis Dimitris Fotakis Stratis Ioannidis 23 10 0 19 Feb 2022
Faster Rates of Private Stochastic Convex Optimization Jinyan Su Lijie Hu Di Wang 21 11 0 31 Jul 2021
High Dimensional Differentially Private Stochastic Optimization with Heavy-tailed Data Lijie Hu Shuo Ni Hanshen Xiao Di Wang 21 51 0 23 Jul 2021
High-Dimensional Differentially-Private EM Algorithm: Methods and Near-Optimal Statistical Guarantees Zhe Zhang Linjun Zhang FedML 37 3 0 01 Apr 2021
Differentially private inference via noisy optimization Marco Avella-Medina Casey Bradshaw Po-Ling Loh FedML 25 29 0 19 Mar 2021
A Central Limit Theorem for Differentially Private Query Answering Jinshuo Dong Weijie J. Su Linjun Zhang 29 15 0 15 Mar 2021
Estimating Smooth GLM in Non-interactive Local Differential Privacy Model with Public Unlabeled Data Di Wang Lijie Hu Huanyu Zhang Marco Gaboardi Jinhui Xu 28 8 0 01 Oct 2019
Privately Learning High-Dimensional Distributions Gautam Kamath Jerry Li Vikrant Singhal Jonathan R. Ullman FedML 69 148 0 01 May 2018