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Private Non-smooth Empirical Risk Minimization and Stochastic Convex Optimization in Subquadratic Steps
29 March 2021
Janardhan Kulkarni
Y. Lee
Daogao Liu
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Papers citing
"Private Non-smooth Empirical Risk Minimization and Stochastic Convex Optimization in Subquadratic Steps"
18 / 18 papers shown
Understanding Private Learning From Feature Perspective
Meng Ding
Mingxi Lei
Shaopeng Fu
Shaowei Wang
Di Wang
Jinhui Xu
MLT
169
1
0
22 Nov 2025
On the Sample Complexity of Differentially Private Policy Optimization
Yi He
Xingyu Zhou
126
0
0
24 Oct 2025
An Iterative Algorithm for Differentially Private
k
k
k
-PCA with Adaptive Noise
Johanna Düngler
Amartya Sanyal
154
0
0
14 Aug 2025
User-level Differentially Private Stochastic Convex Optimization: Efficient Algorithms with Optimal Rates
International Conference on Artificial Intelligence and Statistics (AISTATS), 2023
Hilal Asi
Daogao Liu
245
13
0
07 Nov 2023
Learning across Data Owners with Joint Differential Privacy
Yangsibo Huang
Haotian Jiang
Daogao Liu
Mohammad Mahdian
Jieming Mao
Vahab Mirrokni
FedML
198
0
0
25 May 2023
Near Optimal Private and Robust Linear Regression
Xiyang Liu
Prateek Jain
Weihao Kong
Sewoong Oh
A. Suggala
236
10
0
30 Jan 2023
Differentially Private Online-to-Batch for Smooth Losses
Neural Information Processing Systems (NeurIPS), 2022
Qinzi Zhang
Hoang Tran
Ashok Cutkosky
FedML
235
5
0
12 Oct 2022
Momentum Aggregation for Private Non-convex ERM
Neural Information Processing Systems (NeurIPS), 2022
Hoang Tran
Ashok Cutkosky
203
14
0
12 Oct 2022
Private Stochastic Optimization With Large Worst-Case Lipschitz Parameter: Optimal Rates for (Non-Smooth) Convex Losses and Extension to Non-Convex Losses
Journal of Privacy and Confidentiality (JPC), 2022
Andrew Lowy
Meisam Razaviyayn
374
13
0
15 Sep 2022
When Does Differentially Private Learning Not Suffer in High Dimensions?
Neural Information Processing Systems (NeurIPS), 2022
Xuechen Li
Daogao Liu
Tatsunori Hashimoto
Huseyin A. Inan
Janardhan Kulkarni
Y. Lee
Abhradeep Thakurta
392
62
0
01 Jul 2022
Beyond Uniform Lipschitz Condition in Differentially Private Optimization
International Conference on Machine Learning (ICML), 2022
Rudrajit Das
Satyen Kale
Zheng Xu
Tong Zhang
Sujay Sanghavi
249
22
0
21 Jun 2022
DP-PCA: Statistically Optimal and Differentially Private PCA
Neural Information Processing Systems (NeurIPS), 2022
Xiyang Liu
Weihao Kong
Prateek Jain
Sewoong Oh
351
30
0
27 May 2022
Differentially Private
ℓ
1
\ell_1
ℓ
1
-norm Linear Regression with Heavy-tailed Data
International Symposium on Information Theory (ISIT), 2022
Haiyan Zhao
Jinhui Xu
205
10
0
10 Jan 2022
Faster Rates of Private Stochastic Convex Optimization
Jinyan Su
Lijie Hu
Haiyan Zhao
219
14
0
31 Jul 2021
Differentially Private Stochastic Optimization: New Results in Convex and Non-Convex Settings
Raef Bassily
Cristóbal Guzmán
Michael Menart
262
61
0
12 Jul 2021
Stochastic Bias-Reduced Gradient Methods
Neural Information Processing Systems (NeurIPS), 2021
Hilal Asi
Y. Carmon
A. Jambulapati
Yujia Jin
Aaron Sidford
218
33
0
17 Jun 2021
The Power of Sampling: Dimension-free Risk Bounds in Private ERM
Yin Tat Lee
Daogao Liu
Zhou Lu
408
3
0
28 May 2021
Non-Euclidean Differentially Private Stochastic Convex Optimization: Optimal Rates in Linear Time
Annual Conference Computational Learning Theory (COLT), 2021
Raef Bassily
Cristóbal Guzmán
Anupama Nandi
274
74
0
01 Mar 2021
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