6 citations · 12 across the 16 of their papers we have counts for
18 papers
The Complexity of Nash Equilibrium in Network Congestion and Coordination Games
Ioannis Anagnostides, Ioannis Panageas, Jingming Yan
We show that computing a Nash equilibrium is CLS-complete for linear network congestion and network coordination games. As a result, finding a KKT point of a bilinear polynomial is…
The Computational Complexity of Team Zero-Sum Games
Ioannis Anagnostides, Ioannis Panageas, Tuomas Sandholm +1
A celebrated consequence of the minimax theorem is that two-player zero-sum games admit a tractable equilibrium characterization. In many central applications, however, each side c…
Convex Markov Games and Beyond: New Proof of Existence, Characterization and Learning Algorithms for Nash Equilibria
Anas Barakat, Ioannis Panageas, Antonios Varvitsiotis
Convex Markov Games (cMGs) were recently introduced as a broad class of multi-agent learning problems that generalize Markov games to settings where strategic agents optimize gener…
(Doubly) Exponential Lower Bounds for Follow the Regularized Leader in Potential Games
Ioannis Anagnostides, Ioannis Panageas, Nikolas Patris +1
Follow the regularized leader FTRL is the premier algorithm for online optimization. However, despite decades of research on its convergence in constrained optimization -- and pote…
On the Computational Complexity of Performative Prediction
Ioannis Anagnostides, Rohan Chauhan, Ioannis Panageas +2
Performative prediction captures the phenomenon where deploying a predictive model shifts the underlying data distribution. While simple retraining dynamics are known to converge l…
Convergence of Regret Matching in Potential Games and Constrained Optimization
Ioannis Anagnostides, Emanuel Tewolde, Brian Hu Zhang +3
Regret matching (RM) -- and its modern variants -- is a foundational online algorithm that has been at the heart of many AI breakthrough results in solving benchmark zero-sum games…
The Complexity of Symmetric Equilibria in Min-Max Optimization and Team Zero-Sum Games
Ioannis Anagnostides, Ioannis Panageas, Tuomas Sandholm +1
We consider the problem of computing stationary points in min-max optimization, with a particular focus on the special case of computing Nash equilibria in (two-)team zero-sum game…
Learning Equilibria in Adversarial Team Markov Games: A Nonconvex-Hidden-Concave Min-Max Optimization Problem
Fivos Kalogiannis, Jingming Yan, Ioannis Panageas
We study the problem of learning a Nash equilibrium (NE) in Markov games which is a cornerstone in multi-agent reinforcement learning (MARL). In particular, we focus on infinite-ho…
Computing Nash Equilibria in Potential Games with Private Uncoupled Constraints
Nikolas Patris, Stelios Stavroulakis, Fivos Kalogiannis +2
We consider the problem of computing Nash equilibria in potential games where each player's strategy set is subject to private uncoupled constraints. This scenario is frequently en…
Optimistic Policy Gradient in Multi-Player Markov Games with a Single Controller: Convergence Beyond the Minty Property
Ioannis Anagnostides, Ioannis Panageas, Gabriele Farina +1
Policy gradient methods enjoy strong practical performance in numerous tasks in reinforcement learning. Their theoretical understanding in multiagent settings, however, remains lim…
Zero-sum Polymatrix Markov Games: Equilibrium Collapse and Efficient Computation of Nash Equilibria
Fivos Kalogiannis, Ioannis Panageas
The works of (Daskalakis et al., 2009, 2022; Jin et al., 2022; Deng et al., 2023) indicate that computing Nash equilibria in multi-player Markov games is a computationally hard tas…
On the Convergence of No-Regret Learning Dynamics in Time-Varying Games
Ioannis Anagnostides, Ioannis Panageas, Gabriele Farina +1
Most of the literature on learning in games has focused on the restrictive setting where the underlying repeated game does not change over time. Much less is known about the conver…
Algorithms and Complexity for Computing Nash Equilibria in Adversarial Team Games
Ioannis Anagnostides, Fivos Kalogiannis, Ioannis Panageas +2
Adversarial team games model multiplayer strategic interactions in which a team of identically-interested players is competing against an adversarial player in a zero-sum game. Suc…
Efficiently Computing Nash Equilibria in Adversarial Team Markov Games
Fivos Kalogiannis, Ioannis Anagnostides, Ioannis Panageas +3
Computing Nash equilibrium policies is a central problem in multi-agent reinforcement learning that has received extensive attention both in theory and in practice. However, provab…
On Last-Iterate Convergence Beyond Zero-Sum Games
Ioannis Anagnostides, Ioannis Panageas, Gabriele Farina +1
Most existing results about \emph{last-iterate convergence} of learning dynamics are limited to two-player zero-sum games, and only apply under rigid assumptions about what dynamic…
Optimistic Mirror Descent Either Converges to Nash or to Strong Coarse Correlated Equilibria in Bimatrix Games
Ioannis Anagnostides, Gabriele Farina, Ioannis Panageas +1
We show that, for any sufficiently small fixed , when both players in a general-sum two-player (bimatrix) game employ optimistic mirror descent (OMD) with smooth regularizati…
Towards convergence to Nash equilibria in two-team zero-sum games
Fivos Kalogiannis, Ioannis Panageas, Emmanouil-Vasileios Vlatakis-Gkaragkounis
Contemporary applications of machine learning in two-team e-sports and the superior expressivity of multi-agent generative adversarial networks raise important and overlooked theor…
Frequency-Domain Representation of First-Order Methods: A Simple and Robust Framework of Analysis
Ioannis Anagnostides, Ioannis Panageas
Motivated by recent applications in min-max optimization, we employ tools from nonlinear control theory in order to analyze a class of "historical" gradient-based methods, for whic…