We study the question of obtaining last-iterate convergence rates for no-regret learning
algorithms in multi-player games. We show that the optimistic gradient (OG) algorithm with a …

In this paper, we consider multi-agent learning via online gradient descent in a class of
games called $\lambda $-cocoercive games, a fairly broad class of games that admits many …

We consider online no-regret learning in unknown games with bandit feedback, where each
player can only observe its reward at each time--determined by all players' current joint …

We examine an adaptive learning framework for nonatomic congestion games where the
players' cost functions may be subject to exogenous fluctuations (eg, due to disturbances in …

Online gradient descent (OGD) is well-known to be doubly optimal under strong convexity or
monotonicity assumptions:(1) in the single-agent setting, it achieves an optimal regret of Θ …

Motivated by applications to online advertising and recommender systems, we consider a
game-theoretic model with delayed rewards and asynchronous, payoff-based feedback. In …

We consider centralized and distributed mirror descent (MD) algorithms over a finite-
dimensional Hilbert space, and prove that the problem variables converge to an optimizer of …

Online Mirror Descent (OMD) is an important and widely used class of adaptive learning
algorithms that enjoys good regret performance guarantees. It is therefore natural to study …

We consider a model of game-theoretic learning based on online mirror descent (OMD) with
asynchronous and delayed feedback information. Instead of focusing on specific games, we …

We consider a game-theoretical multi-agent learning problem where the feedback
information can be lost during the learning process and rewards are given by a broad class …