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2005.04763
Cited By
Private Stochastic Convex Optimization: Optimal Rates in Linear Time
10 May 2020
Vitaly Feldman
Tomer Koren
Kunal Talwar
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Papers citing
"Private Stochastic Convex Optimization: Optimal Rates in Linear Time"
50 / 146 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
617
10
0
24 Dec 2025
Efficient Public Verification of Private ML via Regularization
Zoë Ruha Bell
Anvith Thudi
Olive Franzese-McLaughlin
Nicolas Papernot
Shafi Goldwasser
120
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
122
3
0
27 Nov 2025
Understanding Private Learning From Feature Perspective
Meng Ding
Mingxi Lei
Shaopeng Fu
Shaowei Wang
Di Wang
Jinhui Xu
MLT
202
2
0
22 Nov 2025
On the Sample Complexity of Differentially Private Policy Optimization
Yi He
Xingyu Zhou
164
0
0
24 Oct 2025
High-Dimensional Privacy-Utility Dynamics of Noisy Stochastic Gradient Descent on Least Squares
Shurong Lin
E. D. Kolaczyk
Adam D. Smith
Elliot Paquette
155
0
0
19 Oct 2025
Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition
Difei Xu
Meng Ding
Zihang Xiang
Jinhui Xu
Haiyan Zhao
248
2
0
04 Sep 2025
An Iterative Algorithm for Differentially Private
k
k
k
-PCA with Adaptive Noise
Johanna Düngler
Amartya Sanyal
184
1
0
14 Aug 2025
Differentially Private Sparse Linear Regression with Heavy-tailed Responses
Xizhi Tian
Meng Ding
Touming Tao
Zihang Xiang
Di Wang
239
2
0
07 Jun 2025
Privacy-Preserving Federated Convex Optimization: Balancing Partial-Participation and Efficiency via Noise Cancellation
Roie Reshef
Kfir Y. Levy
FedML
269
0
0
03 Jun 2025
Private Geometric Median in Nearly-Linear Time
Syamantak Kumar
Daogao Liu
Kevin Tian
Chutong Yang
FedML
362
0
0
26 May 2025
Second-Order Convergence in Private Stochastic Non-Convex Optimization
Youming Tao
Zuyuan Zhang
Dongxiao Yu
Xiuzhen Cheng
Falko Dressler
Di Wang
326
4
0
21 May 2025
Private Statistical Estimation via Truncation
Manolis Zampetakis
Felix Zhou
315
0
0
18 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
403
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
322
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
222
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
245
2
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
1.1K
2
0
10 Oct 2024
Noise is All You Need: Private Second-Order Convergence of Noisy SGD
Dmitrii Avdiukhin
Michael Dinitz
Chenglin Fan
G. Yaroslavtsev
312
1
0
09 Oct 2024
Improved Sample Complexity for Private Nonsmooth Nonconvex Optimization
Guy Kornowski
Daogao Liu
Kunal Talwar
328
3
0
08 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
223
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
236
1
0
19 Aug 2024
Private and Federated Stochastic Convex Optimization: Efficient Strategies for Centralized Systems
Roie Reshef
Kfir Y. Levy
FedML
290
1
0
17 Jul 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
391
10
0
12 Jul 2024
Privacy of the last iterate in cyclically-sampled DP-SGD on nonconvex composite losses
Weiwei Kong
Mónica Ribero
480
5
0
07 Jul 2024
Private Zeroth-Order Nonsmooth Nonconvex Optimization
Qinzi Zhang
Hoang Tran
Ashok Cutkosky
313
7
0
27 Jun 2024
On Convex Optimization with Semi-Sensitive Features
Badih Ghazi
Pritish Kamath
Ravi Kumar
Pasin Manurangsi
Raghu Meka
Chiyuan Zhang
237
0
0
27 Jun 2024
Private Geometric Median
Mahdi Haghifam
Thomas Steinke
Jonathan R. Ullman
238
2
0
11 Jun 2024
Private Online Learning via Lazy Algorithms
Hilal Asi
Tomer Koren
Daogao Liu
Kunal Talwar
504
3
0
05 Jun 2024
Private Stochastic Convex Optimization with Heavy Tails: Near-Optimality from Simple Reductions
Hilal Asi
Daogao Liu
Kevin Tian
299
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
253
5
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
321
14
0
22 May 2024
Neural Collapse Meets Differential Privacy: Curious Behaviors of NoisyGD with Near-perfect Representation Learning
International Conference on Machine Learning (ICML), 2024
Chendi Wang
Yuqing Zhu
Weijie J. Su
Yu Wang
AAML
293
8
0
14 May 2024
Uniformly Stable Algorithms for Adversarial Training and Beyond
International Conference on Machine Learning (ICML), 2024
Jiancong Xiao
Jiawei Zhang
Zhimin Luo
Asuman Ozdaglar
AAML
263
2
0
03 May 2024
FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneity
Kai Yi
Nidham Gazagnadou
Peter Richtárik
Lingjuan Lyu
356
18
0
15 Apr 2024
Differentially Private Worst-group Risk Minimization
Xinyu Zhou
Raef Bassily
239
7
0
29 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
376
11
0
17 Feb 2024
Differentially Private SGD Without Clipping Bias: An Error-Feedback Approach
International Conference on Learning Representations (ICLR), 2023
Xinwei Zhang
Zhiqi Bu
Zhiwei Steven Wu
Mingyi Hong
298
13
0
24 Nov 2023
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
370
3
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
283
15
0
07 Nov 2023
Optimal Guarantees for Algorithmic Reproducibility and Gradient Complexity in Convex Optimization
Neural Information Processing Systems (NeurIPS), 2023
Liang Zhang
Junchi Yang
Amin Karbasi
Niao He
440
3
0
26 Oct 2023
Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy
Yingyu Lin
Yian Ma
Yu-Xiang Wang
Rachel Redberg
Zhiqi Bu
368
4
0
23 Oct 2023
DPZero: Private Fine-Tuning of Language Models without Backpropagation
Liang Zhang
Bingcong Li
K. K. Thekumparampil
Sewoong Oh
Niao He
527
24
0
14 Oct 2023
Differentially Private Non-convex Learning for Multi-layer Neural Networks
Hanpu Shen
Cheng-Long Wang
Zihang Xiang
Yiming Ying
Di Wang
406
9
0
12 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
237
5
0
11 Oct 2023
Tight Bounds for Machine Unlearning via Differential Privacy
Journal of Privacy and Confidentiality (JPC), 2023
Yiyang Huang
C. Canonne
MU
314
20
0
02 Sep 2023
The Relative Gaussian Mechanism and its Application to Private Gradient Descent
International Conference on Artificial Intelligence and Statistics (AISTATS), 2023
Aymeric Dieuleveut
Paul Mangold
A. Bellet
441
1
0
29 Aug 2023
The importance of feature preprocessing for differentially private linear optimization
International Conference on Learning Representations (ICLR), 2023
Ziteng Sun
A. Suresh
A. Menon
296
4
0
19 Jul 2023
Safeguarding Data in Multimodal AI: A Differentially Private Approach to CLIP Training
Alyssa Huang
Peihan Liu
Ryumei Nakada
Linjun Zhang
Wanrong Zhang
VLM
461
8
0
13 Jun 2023
(Amplified) Banded Matrix Factorization: A unified approach to private training
Neural Information Processing Systems (NeurIPS), 2023
Christopher A. Choquette-Choo
Arun Ganesh
Ryan McKenna
H. B. McMahan
Keith Rush
Abhradeep Thakurta
Zheng Xu
FedML
531
59
0
13 Jun 2023
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