We consider online learning in multi-player smooth monotone games. Existing algorithms
have limitations such as (1) being only applicable to strongly monotone games;(2) lacking …

The existence of simple, uncoupled no-regret dynamics that converge to correlated
equilibria in normal-form games is a celebrated result in the theory of multi-agent systems …

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 routing …

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 …

In this work, we propose a universal and adaptive second-order method for minimization of
second-order smooth, convex functions. Precisely, our algorithm achieves $ O (\sigma/\sqrt …

This paper proposes Mutation-Driven Multiplicative Weights Update (M2WU) for learning an
equilibrium in two-player zero-sum normal-form games and proves that it exhibits the last …

This work is dedicated to the algorithm design in a competitive framework, with the primary
goal of learning a stable equilibrium. We consider the dynamic price competition between …

Numerous applications in machine learning and data analytics can be formulated as
equilibrium computation over Riemannian manifolds. Despite the extensive investigation of …

We address learning Nash equilibria in convex games under the payoff information setting.
We consider the case in which the game pseudo-gradient is monotone but not necessarily …

In view of the complexity of the dynamics of learning in games, we seek to decompose a
game into simpler components where the dynamics' long-run behavior is well understood. A …