17 citations · 38 across the 18 of their papers we have counts for
12 papers · 1 filter
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…
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…
Meta-Learning in Games
Keegan Harris, Ioannis Anagnostides, Gabriele Farina +3
In the literature on game-theoretic equilibrium finding, focus has mainly been on solving a single game in isolation. In practice, however, strategic interactions -- ranging from r…
Sampling and Optimal Preference Elicitation in Simple Mechanisms
Ioannis Anagnostides, Dimitris Fotakis, Panagiotis Patsilinakos
In this work we are concerned with the design of efficient mechanisms while eliciting limited information from the agents. First, we study the performance of sampling approximation…
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…
Near-Optimal -Regret Learning in Extensive-Form Games
Ioannis Anagnostides, Gabriele Farina, Tuomas Sandholm
In this paper, we establish efficient and uncoupled learning dynamics so that, when employed by all players in multiplayer perfect-recall imperfect-information extensive-form games…
Near-Optimal No-Regret Learning Dynamics for General Convex Games
Gabriele Farina, Ioannis Anagnostides, Haipeng Luo +3
A recent line of work has established uncoupled learning dynamics such that, when employed by all players in a game, each player's \emph{regret} after repetitions grows polylog…
Uncoupled Learning Dynamics with Swap Regret in Multiplayer Games
Ioannis Anagnostides, Gabriele Farina, Christian Kroer +3
In this paper we establish efficient and \emph{uncoupled} learning dynamics so that, when employed by all players in a general-sum multiplayer game, the \emph{swap regret} of each…
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…
Dimensionality, Coordination, and Robustness in Voting
Ioannis Anagnostides, Dimitris Fotakis, Panagiotis Patsilinakos
We study the performance of voting mechanisms from a utilitarian standpoint, under the recently introduced framework of metric-distortion, offering new insights along three main li…
Metric-Distortion Bounds under Limited Information
Ioannis Anagnostides, Dimitris Fotakis, Panagiotis Patsilinakos
In this work we study the metric distortion problem in voting theory under a limited amount of ordinal information. Our primary contribution is threefold. First, we consider mechan…