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2103.01516
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Private Stochastic Convex Optimization: Optimal Rates in
ℓ
1
\ell_1
ℓ
1
Geometry
International Conference on Machine Learning (ICML), 2021
2 March 2021
Hilal Asi
Vitaly Feldman
Tomer Koren
Kunal Talwar
Re-assign community
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Papers citing
"Private Stochastic Convex Optimization: Optimal Rates in $\ell_1$ Geometry"
21 / 71 papers shown
Efficient Private SCO for Heavy-Tailed Data via Clipping
Machine-mediated learning (ML), 2022
Chenhan Jin
Kaiwen Zhou
Bo Han
Ming Yang
James Cheng
170
3
0
27 Jun 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
On Private Online Convex Optimization: Optimal Algorithms in
ℓ
p
\ell_p
ℓ
p
-Geometry and High Dimensional Contextual Bandits
Yuxuan Han
Zhicong Liang
Zhipeng Liang
Yang Wang
Xingtai Lv
Jiheng Zhang
199
1
0
16 Jun 2022
Faster Rates of Convergence to Stationary Points in Differentially Private Optimization
International Conference on Machine Learning (ICML), 2022
R. Arora
Raef Bassily
Tomás González
Cristóbal Guzmán
Michael Menart
Enayat Ullah
182
35
0
02 Jun 2022
Bring Your Own Algorithm for Optimal Differentially Private Stochastic Minimax Optimization
Neural Information Processing Systems (NeurIPS), 2022
Liang Zhang
K. K. Thekumparampil
Sewoong Oh
Niao He
267
21
0
01 Jun 2022
Unlocking High-Accuracy Differentially Private Image Classification through Scale
Soham De
Leonard Berrada
Jamie Hayes
Samuel L. Smith
Borja Balle
407
267
0
28 Apr 2022
Differentially Private Learning with Margin Guarantees
Neural Information Processing Systems (NeurIPS), 2022
Raef Bassily
M. Mohri
A. Suresh
160
10
0
21 Apr 2022
Private Convex Optimization via Exponential Mechanism
Annual Conference Computational Learning Theory (COLT), 2022
Sivakanth Gopi
Y. Lee
Daogao Liu
320
59
0
01 Mar 2022
Constant matters: Fine-grained Complexity of Differentially Private Continual Observation
International Conference on Machine Learning (ICML), 2022
Hendrik Fichtenberger
Monika Henzinger
Jalaj Upadhyay
674
32
0
23 Feb 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
Differentially Private Coordinate Descent for Composite Empirical Risk Minimization
International Conference on Machine Learning (ICML), 2021
Paul Mangold
A. Bellet
Joseph Salmon
Marc Tommasi
507
16
0
22 Oct 2021
Adapting to Function Difficulty and Growth Conditions in Private Optimization
Neural Information Processing Systems (NeurIPS), 2021
Hilal Asi
Daniel Levy
John C. Duchi
164
25
0
05 Aug 2021
High Dimensional Differentially Private Stochastic Optimization with Heavy-tailed Data
ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems (PODS), 2021
Lijie Hu
Shuo Ni
Hanshen Xiao
Haiyan Zhao
305
58
0
23 Jul 2021
Differentially Private Stochastic Optimization: New Results in Convex and Non-Convex Settings
Raef Bassily
Cristóbal Guzmán
Michael Menart
262
60
0
12 Jul 2021
Private Adaptive Gradient Methods for Convex Optimization
International Conference on Machine Learning (ICML), 2021
Hilal Asi
John C. Duchi
Alireza Fallah
O. Javidbakht
Kunal Talwar
221
61
0
25 Jun 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
On the Convergence of Differentially Private Federated Learning on Non-Lipschitz Objectives, and with Normalized Client Updates
Rudrajit Das
Abolfazl Hashemi
Sujay Sanghavi
Inderjit S. Dhillon
FedML
183
4
0
13 Jun 2021
The Power of Sampling: Dimension-free Risk Bounds in Private ERM
Yin Tat Lee
Daogao Liu
Zhou Lu
405
3
0
28 May 2021
Optimal Algorithms for Differentially Private Stochastic Monotone Variational Inequalities and Saddle-Point Problems
Mathematical programming (Math. Program.), 2021
Digvijay Boob
Cristóbal Guzmán
345
20
0
07 Apr 2021
Private Non-smooth Empirical Risk Minimization and Stochastic Convex Optimization in Subquadratic Steps
Janardhan Kulkarni
Y. Lee
Daogao Liu
176
32
0
29 Mar 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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