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2103.01516
Cited By
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
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Papers citing
"Private Stochastic Convex Optimization: Optimal Rates in $\ell_1$ Geometry"
50 / 71 papers shown
Algorithmic Aspects of the Log-Laplace Transform and a Non-Euclidean Proximal Sampler
Annual Conference Computational Learning Theory (COLT), 2023
Sivakanth Gopi
Y. Lee
Daogao Liu
Ruoqi Shen
Kevin Tian
470
9
0
24 Dec 2025
Efficient Public Verification of Private ML via Regularization
Zoë Ruha Bell
Anvith Thudi
Olive Franzese-McLaughlin
Nicolas Papernot
Shafi Goldwasser
70
0
0
03 Dec 2025
Towards Understanding Generalization in DP-GD: A Case Study in Training Two-Layer CNNs
Zhongjie Shi
Puyu Wang
Chenyang Zhang
Yuan Cao
63
0
0
27 Nov 2025
On the Sample Complexity of Differentially Private Policy Optimization
Yi He
Xingyu Zhou
120
0
0
24 Oct 2025
Towards Reliable and Generalizable Differentially Private Machine Learning (Extended Version)
Wenxuan Bao
Vincent Bindschaedler
AAML
241
0
0
21 Aug 2025
Private Geometric Median in Nearly-Linear Time
Syamantak Kumar
Daogao Liu
Kevin Tian
Chutong Yang
FedML
287
0
0
26 May 2025
Purifying Approximate Differential Privacy with Randomized Post-processing
IEEE Transactions on Visualization and Computer Graphics (TVCG), 2025
Yingyu Lin
Erchi Wang
Yi-An Ma
Yu-Xiang Wang
309
2
0
27 Mar 2025
Characterizing the Accuracy-Communication-Privacy Trade-off in Distributed Stochastic Convex Optimization
International Conference on Artificial Intelligence and Statistics (AISTATS), 2025
Sudeep Salgia
Nikola Pavlovic
Yuejie Chi
Qing Zhao
294
0
0
06 Jan 2025
Private Algorithms for Stochastic Saddle Points and Variational Inequalities: Beyond Euclidean Geometry
Neural Information Processing Systems (NeurIPS), 2024
Raef Bassily
Cristóbal Guzmán
Michael Menart
179
2
0
07 Nov 2024
Faster Algorithms for User-Level Private Stochastic Convex Optimization
Neural Information Processing Systems (NeurIPS), 2024
Andrew Lowy
Daogao Liu
Hilal Asi
193
1
0
24 Oct 2024
Adaptive Batch Size for Privately Finding Second-Order Stationary Points
International Conference on Learning Representations (ICLR), 2024
Daogao Liu
Kunal Talwar
934
1
0
10 Oct 2024
Federated Online Prediction from Experts with Differential Privacy: Separations and Regret Speed-ups
Neural Information Processing Systems (NeurIPS), 2024
Fengyu Gao
Ruiquan Huang
Jing Yang
FedML
198
1
0
27 Sep 2024
Differential Private Stochastic Optimization with Heavy-tailed Data: Towards Optimal Rates
AAAI Conference on Artificial Intelligence (AAAI), 2024
Puning Zhao
Yan Han
Zhe Liu
Chong Wang
Rongfei Fan
Qingming Li
205
1
0
19 Aug 2024
Private Heterogeneous Federated Learning Without a Trusted Server Revisited: Error-Optimal and Communication-Efficient Algorithms for Convex Losses
Changyu Gao
Andrew Lowy
Xingyu Zhou
Stephen J. Wright
FedML
279
10
0
12 Jul 2024
Fast Rates for Bandit PAC Multiclass Classification
Liad Erez
Alon Cohen
Tomer Koren
Yishay Mansour
Shay Moran
168
3
0
18 Jun 2024
Private Online Learning via Lazy Algorithms
Hilal Asi
Tomer Koren
Daogao Liu
Kunal Talwar
465
3
0
05 Jun 2024
Private Stochastic Convex Optimization with Heavy Tails: Near-Optimality from Simple Reductions
Hilal Asi
Daogao Liu
Kevin Tian
236
7
0
04 Jun 2024
Learning with User-Level Local Differential Privacy
Puning Zhao
Li Shen
Rongfei Fan
Qingming Li
Huiwen Wu
Yan Han
Zhe Liu
216
4
0
27 May 2024
A Huber Loss Minimization Approach to Mean Estimation under User-level Differential Privacy
Puning Zhao
Lifeng Lai
Li Shen
Qingming Li
Yan Han
Zhe Liu
278
12
0
22 May 2024
SoK: A Review of Differentially Private Linear Models For High-Dimensional Data
Amol Khanna
Edward Raff
Nathan Inkawhich
248
5
0
01 Apr 2024
Mirror Descent Algorithms with Nearly Dimension-Independent Rates for Differentially-Private Stochastic Saddle-Point Problems
Annual Conference Computational Learning Theory (COLT), 2024
Tomás González
Cristóbal Guzmán
Courtney Paquette
276
8
0
05 Mar 2024
SPriFed-OMP: A Differentially Private Federated Learning Algorithm for Sparse Basis Recovery
Ajinkya Kiran Mulay
Xiaojun Lin
246
0
0
29 Feb 2024
Taming Nonconvex Stochastic Mirror Descent with General Bregman Divergence
Ilyas Fatkhullin
Niao He
298
13
0
27 Feb 2024
How to Make the Gradients Small Privately: Improved Rates for Differentially Private Non-Convex Optimization
Andrew Lowy
Jonathan R. Ullman
Stephen J. Wright
306
10
0
17 Feb 2024
Differentially Private Non-Convex Optimization under the KL Condition with Optimal Rates
International Conference on Algorithmic Learning Theory (ALT), 2023
Michael Menart
Enayat Ullah
Raman Arora
Raef Bassily
Cristóbal Guzmán
307
2
0
22 Nov 2023
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
Initialization Matters: Privacy-Utility Analysis of Overparameterized Neural Networks
Neural Information Processing Systems (NeurIPS), 2023
Jiayuan Ye
Zhenyu Zhu
Fanghui Liu
Reza Shokri
Volkan Cevher
228
16
0
31 Oct 2023
Scaling Up Differentially Private LASSO Regularized Logistic Regression via Faster Frank-Wolfe Iterations
Neural Information Processing Systems (NeurIPS), 2023
Edward Raff
Amol Khanna
Fred Lu
211
10
0
30 Oct 2023
DPZero: Private Fine-Tuning of Language Models without Backpropagation
Liang Zhang
Bingcong Li
K. K. Thekumparampil
Sewoong Oh
Niao He
457
22
0
14 Oct 2023
Improved Analysis of Sparse Linear Regression in Local Differential Privacy Model
International Conference on Learning Representations (ICLR), 2023
Liyang Zhu
Meng Ding
Vaneet Aggarwal
Jinhui Xu
Haiyan Zhao
195
5
0
11 Oct 2023
Controlling Federated Learning for Covertness
Adit Jain
Vikram Krishnamurthy
FedML
158
6
0
17 Aug 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
On User-Level Private Convex Optimization
International Conference on Machine Learning (ICML), 2023
Badih Ghazi
Pritish Kamath
Ravi Kumar
Raghu Meka
Pasin Manurangsi
Chiyuan Zhang
FedML
212
10
0
08 May 2023
Differentially Private Stochastic Convex Optimization in (Non)-Euclidean Space Revisited
Conference on Uncertainty in Artificial Intelligence (UAI), 2023
Jinyan Su
Changhong Zhao
Haiyan Zhao
208
7
0
31 Mar 2023
Differentially Private Algorithms for the Stochastic Saddle Point Problem with Optimal Rates for the Strong Gap
Annual Conference Computational Learning Theory (COLT), 2023
Raef Bassily
Cristóbal Guzmán
Michael Menart
FedML
263
9
0
24 Feb 2023
Private (Stochastic) Non-Convex Optimization Revisited: Second-Order Stationary Points and Excess Risks
Neural Information Processing Systems (NeurIPS), 2023
Arun Ganesh
Daogao Liu
Sewoong Oh
Abhradeep Thakurta
ODL
211
14
0
20 Feb 2023
DIFF2: Differential Private Optimization via Gradient Differences for Nonconvex Distributed Learning
International Conference on Machine Learning (ICML), 2023
Tomoya Murata
Taiji Suzuki
245
11
0
08 Feb 2023
Near Optimal Private and Robust Linear Regression
Xiyang Liu
Prateek Jain
Weihao Kong
Sewoong Oh
A. Suggala
233
10
0
30 Jan 2023
ReSQueing Parallel and Private Stochastic Convex Optimization
IEEE Annual Symposium on Foundations of Computer Science (FOCS), 2023
Y. Carmon
A. Jambulapati
Yujia Jin
Y. Lee
Daogao Liu
Aaron Sidford
Kevin Tian
FedML
359
18
0
01 Jan 2023
Optimal Algorithms for Stochastic Complementary Composite Minimization
SIAM Journal on Optimization (SIAM J. Optim.), 2022
Alexandre d’Aspremont
Cristóbal Guzmán
Clément Lezane
218
3
0
03 Nov 2022
Private optimization in the interpolation regime: faster rates and hardness results
International Conference on Machine Learning (ICML), 2022
Hilal Asi
Karan N. Chadha
Gary Cheng
John C. Duchi
202
5
0
31 Oct 2022
Private Online Prediction from Experts: Separations and Faster Rates
Annual Conference Computational Learning Theory (COLT), 2022
Hilal Asi
Vitaly Feldman
Tomer Koren
Kunal Talwar
FedML
247
23
0
24 Oct 2022
Stochastic Differentially Private and Fair Learning
International Conference on Learning Representations (ICLR), 2022
Andrew Lowy
Devansh Gupta
Meisam Razaviyayn
FaML
FedML
290
18
0
17 Oct 2022
PAC Privacy: Automatic Privacy Measurement and Control of Data Processing
Annual International Cryptology Conference (CRYPTO), 2022
Hanshen Xiao
S. Devadas
319
21
0
07 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
371
13
0
15 Sep 2022
Private Domain Adaptation from a Public Source
Raef Bassily
M. Mohri
A. Suresh
107
4
0
12 Aug 2022
Private Convex Optimization in General Norms
ACM-SIAM Symposium on Discrete Algorithms (SODA), 2022
Sivakanth Gopi
Y. Lee
Daogao Liu
Ruoqi Shen
Kevin Tian
209
16
0
18 Jul 2022
Uniform Stability for First-Order Empirical Risk Minimization
Annual Conference Computational Learning Theory (COLT), 2022
Amit Attia
Tomer Koren
157
8
0
17 Jul 2022
High-Dimensional Private Empirical Risk Minimization by Greedy Coordinate Descent
International Conference on Artificial Intelligence and Statistics (AISTATS), 2022
Paul Mangold
A. Bellet
Joseph Salmon
Marc Tommasi
433
6
0
04 Jul 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
390
62
0
01 Jul 2022
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