ResearchTrend.AI
The Cost of Privacy in Generalized Linear Models: Algorithms and Minimax Lower Bounds
v1v2 (latest)

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

T. Tony Cai
Yichen Wang
Linjun Zhang
ArXiv (abs)PDFHTML

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

13 / 13 papers shown
Title
Differentially Private Sparse Linear Regression with Heavy-tailed Responses
Differentially Private Sparse Linear Regression with Heavy-tailed Responses
Xizhi Tian
Meng Ding
Touming Tao
Zihang Xiang
Di Wang
18
0
0
07 Jun 2025
Differentially Private High Dimensional Bandits
Differentially Private High Dimensional Bandits
Apurv Shukla
49
0
0
06 Feb 2024
Truthful Generalized Linear Models
Truthful Generalized Linear Models
Yuan Qiu
Jinyan Liu
Di Wang
FedML
78
2
0
16 Sep 2022
Fast Composite Optimization and Statistical Recovery in Federated Learning
Fast Composite Optimization and Statistical Recovery in Federated Learning
Yajie Bao
M. Crawshaw
Sha Luo
Mingrui Liu
FedML
89
17
0
17 Jul 2022
Hypothesis Testing for Differentially Private Linear Regression
Hypothesis Testing for Differentially Private Linear Regression
Daniel Alabi
Salil P. Vadhan
FedML
73
13
0
29 Jun 2022
New Lower Bounds for Private Estimation and a Generalized Fingerprinting Lemma
New Lower Bounds for Private Estimation and a Generalized Fingerprinting Lemma
Gautam Kamath
Argyris Mouzakis
Vikrant Singhal
FedML
88
30
0
17 May 2022
Differentially Private Regression with Unbounded Covariates
Differentially Private Regression with Unbounded Covariates
Jason Milionis
Alkis Kalavasis
Dimitris Fotakis
Stratis Ioannidis
65
11
0
19 Feb 2022
Faster Rates of Private Stochastic Convex Optimization
Faster Rates of Private Stochastic Convex Optimization
Jinyan Su
Lijie Hu
Di Wang
62
13
0
31 Jul 2021
High Dimensional Differentially Private Stochastic Optimization with Heavy-tailed Data
High Dimensional Differentially Private Stochastic Optimization with Heavy-tailed Data
Lijie Hu
Shuo Ni
Hanshen Xiao
Di Wang
142
53
0
23 Jul 2021
High-Dimensional Differentially-Private EM Algorithm: Methods and Near-Optimal Statistical Guarantees
High-Dimensional Differentially-Private EM Algorithm: Methods and Near-Optimal Statistical Guarantees
Zhe Zhang
Linjun Zhang
FedML
70
3
0
01 Apr 2021
Differentially private inference via noisy optimization
Differentially private inference via noisy optimization
Marco Avella-Medina
Casey Bradshaw
Po-Ling Loh
FedML
79
31
0
19 Mar 2021
A Central Limit Theorem for Differentially Private Query Answering
A Central Limit Theorem for Differentially Private Query Answering
Jinshuo Dong
Weijie J. Su
Linjun Zhang
83
15
0
15 Mar 2021
Estimating Smooth GLM in Non-interactive Local Differential Privacy Model with Public Unlabeled Data
Estimating Smooth GLM in Non-interactive Local Differential Privacy Model with Public Unlabeled Data
Di Wang
Lijie Hu
Huanyu Zhang
Marco Gaboardi
Jinhui Xu
127
9
0
01 Oct 2019