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Minimax Optimization with Smooth Algorithmic Adversaries

Minimax Optimization with Smooth Algorithmic Adversaries

2 June 2021
Tanner Fiez
Chi Jin
Praneeth Netrapalli
Lillian J. Ratliff
    AAML
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Papers citing "Minimax Optimization with Smooth Algorithmic Adversaries"

5 / 5 papers shown
Title
Two-Timescale Gradient Descent Ascent Algorithms for Nonconvex Minimax Optimization
Two-Timescale Gradient Descent Ascent Algorithms for Nonconvex Minimax Optimization
Tianyi Lin
Chi Jin
Michael I. Jordan
52
6
0
28 Jan 2025
On adversarial robustness and the use of Wasserstein ascent-descent
  dynamics to enforce it
On adversarial robustness and the use of Wasserstein ascent-descent dynamics to enforce it
Camilo A. Garcia Trillos
Nicolas García Trillos
21
5
0
09 Jan 2023
Proximal Learning With Opponent-Learning Awareness
Proximal Learning With Opponent-Learning Awareness
S. Zhao
Chris Xiaoxuan Lu
Roger C. Grosse
Jakob N. Foerster
34
21
0
18 Oct 2022
On Solving Minimax Optimization Locally: A Follow-the-Ridge Approach
On Solving Minimax Optimization Locally: A Follow-the-Ridge Approach
Yuanhao Wang
Guodong Zhang
Jimmy Ba
33
100
0
16 Oct 2019
Adversarial Machine Learning at Scale
Adversarial Machine Learning at Scale
Alexey Kurakin
Ian Goodfellow
Samy Bengio
AAML
273
3,110
0
04 Nov 2016
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