2 citations · 3 across the 8 of their papers we have counts for
8 papers
Revenge of Monosemanticity: Neuron Specialization as a New Form of Feature Learning in MLPs
Amirhesam Abedsoltan, Enric Boix-Adsera, Fivos Kalogiannis +1
Understanding how neural networks learn and organize features is central to understanding their behavior. Much existing theory of feature learning has focused on the emergence of a…
Solving Zero-Sum Convex Markov Games
Fivos Kalogiannis, Emmanouil-Vasileios Vlatakis-Gkaragkounis, Ian Gemp +1
We contribute the first provable guarantees of global convergence to Nash equilibria (NE) in two-player zero-sum convex Markov games (cMGs) by using independent policy gradient met…
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…
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…
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…