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Momentum Aggregation for Private Non-convex ERM

Momentum Aggregation for Private Non-convex ERM

12 October 2022
Hoang Tran
Ashok Cutkosky
ArXivPDFHTML

Papers citing "Momentum Aggregation for Private Non-convex ERM"

13 / 13 papers shown
Title
Adaptive Batch Size for Privately Finding Second-Order Stationary Points
Adaptive Batch Size for Privately Finding Second-Order Stationary Points
Daogao Liu
Kunal Talwar
130
0
0
10 Oct 2024
Differentially Private Bilevel Optimization
Differentially Private Bilevel Optimization
Guy Kornowski
142
0
0
29 Sep 2024
Empirical Tests of Optimization Assumptions in Deep Learning
Empirical Tests of Optimization Assumptions in Deep Learning
Hoang Tran
Qinzi Zhang
Ashok Cutkosky
31
1
0
01 Jul 2024
Private Zeroth-Order Nonsmooth Nonconvex Optimization
Private Zeroth-Order Nonsmooth Nonconvex Optimization
Qinzi Zhang
Hoang Tran
Ashok Cutkosky
40
4
0
27 Jun 2024
How to Make the Gradients Small Privately: Improved Rates for
  Differentially Private Non-Convex Optimization
How to Make the Gradients Small Privately: Improved Rates for Differentially Private Non-Convex Optimization
Andrew Lowy
Jonathan R. Ullman
Stephen J. Wright
41
6
0
17 Feb 2024
DPZero: Private Fine-Tuning of Language Models without Backpropagation
DPZero: Private Fine-Tuning of Language Models without Backpropagation
Liang Zhang
Bingcong Li
K. K. Thekumparampil
Sewoong Oh
Niao He
28
11
0
14 Oct 2023
Private (Stochastic) Non-Convex Optimization Revisited: Second-Order
  Stationary Points and Excess Risks
Private (Stochastic) Non-Convex Optimization Revisited: Second-Order Stationary Points and Excess Risks
Arun Ganesh
Daogao Liu
Sewoong Oh
Abhradeep Thakurta
ODL
27
12
0
20 Feb 2023
DIFF2: Differential Private Optimization via Gradient Differences for
  Nonconvex Distributed Learning
DIFF2: Differential Private Optimization via Gradient Differences for Nonconvex Distributed Learning
Tomoya Murata
Taiji Suzuki
22
8
0
08 Feb 2023
Gradient Descent with Linearly Correlated Noise: Theory and Applications
  to Differential Privacy
Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy
Anastasia Koloskova
Ryan McKenna
Zachary B. Charles
Keith Rush
Brendan McMahan
27
8
0
02 Feb 2023
Beyond Uniform Lipschitz Condition in Differentially Private
  Optimization
Beyond Uniform Lipschitz Condition in Differentially Private Optimization
Rudrajit Das
Satyen Kale
Zheng Xu
Tong Zhang
Sujay Sanghavi
24
17
0
21 Jun 2022
Faster Rates of Convergence to Stationary Points in Differentially
  Private Optimization
Faster Rates of Convergence to Stationary Points in Differentially Private Optimization
R. Arora
Raef Bassily
Tomás González
Cristóbal Guzmán
Michael Menart
Enayat Ullah
21
29
0
02 Jun 2022
Differentially Private Stochastic Optimization: New Results in Convex
  and Non-Convex Settings
Differentially Private Stochastic Optimization: New Results in Convex and Non-Convex Settings
Raef Bassily
Cristóbal Guzmán
Michael Menart
39
54
0
12 Jul 2021
Practical and Private (Deep) Learning without Sampling or Shuffling
Practical and Private (Deep) Learning without Sampling or Shuffling
Peter Kairouz
Brendan McMahan
Shuang Song
Om Thakkar
Abhradeep Thakurta
Zheng Xu
FedML
182
154
0
26 Feb 2021
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