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On the Impossibility of Global Convergence in Multi-Loss Optimization

On the Impossibility of Global Convergence in Multi-Loss Optimization

26 May 2020
Alistair Letcher
ArXivPDFHTML

Papers citing "On the Impossibility of Global Convergence in Multi-Loss Optimization"

19 / 19 papers shown
Title
Analysing the Sample Complexity of Opponent Shaping
Analysing the Sample Complexity of Opponent Shaping
Kitty Fung
Qizhen Zhang
Chris Xiaoxuan Lu
Jia Wan
Timon Willi
Jakob N. Foerster
26
0
0
08 Feb 2024
The Danger Of Arrogance: Welfare Equilibra As A Solution To Stackelberg
  Self-Play In Non-Coincidental Games
The Danger Of Arrogance: Welfare Equilibra As A Solution To Stackelberg Self-Play In Non-Coincidental Games
Jake Levi
Chris Xiaoxuan Lu
Timon Willi
Christian Schroeder de Witt
Jakob N. Foerster
28
0
0
02 Feb 2024
Exploiting hidden structures in non-convex games for convergence to Nash
  equilibrium
Exploiting hidden structures in non-convex games for convergence to Nash equilibrium
Iosif Sakos
Emmanouil-Vasileios Vlatakis-Gkaragkounis
P. Mertikopoulos
Georgios Piliouras
14
4
0
27 Dec 2023
Chaos persists in large-scale multi-agent learning despite adaptive
  learning rates
Chaos persists in large-scale multi-agent learning despite adaptive learning rates
Emmanouil-Vasileios Vlatakis-Gkaragkounis
Lampros Flokas
Georgios Piliouras
10
1
0
01 Jun 2023
Alternating Mirror Descent for Constrained Min-Max Games
Alternating Mirror Descent for Constrained Min-Max Games
Andre Wibisono
Molei Tao
Georgios Piliouras
24
14
0
08 Jun 2022
Competitive Gradient Optimization
Competitive Gradient Optimization
Abhijeet Vyas
Kamyar Azizzadenesheli
6
1
0
27 May 2022
Nash, Conley, and Computation: Impossibility and Incompleteness in Game
  Dynamics
Nash, Conley, and Computation: Impossibility and Incompleteness in Game Dynamics
Jason Milionis
Christos H. Papadimitriou
Georgios Piliouras
Kelly Spendlove
11
9
0
26 Mar 2022
No-Regret Learning in Games is Turing Complete
No-Regret Learning in Games is Turing Complete
Gabriel P. Andrade
Rafael M. Frongillo
Georgios Piliouras
11
7
0
24 Feb 2022
Multi-agent Performative Prediction: From Global Stability and
  Optimality to Chaos
Multi-agent Performative Prediction: From Global Stability and Optimality to Chaos
Georgios Piliouras
Fang-Yi Yu
40
34
0
25 Jan 2022
Faster Single-loop Algorithms for Minimax Optimization without Strong
  Concavity
Faster Single-loop Algorithms for Minimax Optimization without Strong Concavity
Junchi Yang
Antonio Orvieto
Aurelien Lucchi
Niao He
24
62
0
10 Dec 2021
Neural Network Weights Do Not Converge to Stationary Points: An
  Invariant Measure Perspective
Neural Network Weights Do Not Converge to Stationary Points: An Invariant Measure Perspective
Junzhe Zhang
Haochuan Li
S. Sra
Ali Jadbabaie
66
9
0
12 Oct 2021
Constants of Motion: The Antidote to Chaos in Optimization and Game
  Dynamics
Constants of Motion: The Antidote to Chaos in Optimization and Game Dynamics
Georgios Piliouras
Xiao Wang
26
0
0
08 Sep 2021
A Game-Theoretic Approach to Multi-Agent Trust Region Optimization
A Game-Theoretic Approach to Multi-Agent Trust Region Optimization
Ying Wen
Hui Chen
Yaodong Yang
Zheng Tian
Minne Li
Xu Chen
Jun Wang
38
11
0
12 Jun 2021
Minimax Optimization with Smooth Algorithmic Adversaries
Minimax Optimization with Smooth Algorithmic Adversaries
Tanner Fiez
Chi Jin
Praneeth Netrapalli
Lillian J. Ratliff
AAML
16
11
0
02 Jun 2021
Learning in Matrix Games can be Arbitrarily Complex
Learning in Matrix Games can be Arbitrarily Complex
Gabriel P. Andrade
Rafael M. Frongillo
Georgios Piliouras
15
31
0
05 Mar 2021
ScrofaZero: Mastering Trick-taking Poker Game Gongzhu by Deep
  Reinforcement Learning
ScrofaZero: Mastering Trick-taking Poker Game Gongzhu by Deep Reinforcement Learning
Naichen Shi
Ruichen Li
Sun Youran
11
0
0
15 Feb 2021
Limiting Behaviors of Nonconvex-Nonconcave Minimax Optimization via
  Continuous-Time Systems
Limiting Behaviors of Nonconvex-Nonconcave Minimax Optimization via Continuous-Time Systems
Benjamin Grimmer
Haihao Lu
Pratik Worah
Vahab Mirrokni
26
7
0
20 Oct 2020
The limits of min-max optimization algorithms: convergence to spurious
  non-critical sets
The limits of min-max optimization algorithms: convergence to spurious non-critical sets
Ya-Ping Hsieh
P. Mertikopoulos
V. Cevher
27
81
0
16 Jun 2020
First-order Methods Almost Always Avoid Saddle Points
First-order Methods Almost Always Avoid Saddle Points
J. Lee
Ioannis Panageas
Georgios Piliouras
Max Simchowitz
Michael I. Jordan
Benjamin Recht
ODL
93
82
0
20 Oct 2017
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