Learning in stochastic games is a notoriously difficult problem because, in addition to each
other's strategic decisions, the players must also contend with the fact that the game itself …

In this paper, we examine the convergence rate of a wide range of regularized methods for
learning in games. To that end, we propose a unified algorithmic template that we call …

The recent framework of performative prediction [Perdomo et al. 2020] is aimed at capturing
settings where predictions influence the outcome they want to predict. In this paper, we …

In this paper, we examine the Nash equilibrium convergence properties of no-regret
learning in general N-player games. For concreteness, we focus on the archetypal “follow …

We develop a flexible stochastic approximation framework for analyzing the long-run
behavior of learning in games (both continuous and finite). The proposed analysis template …

In this paper, we examine the long-run behavior of regularized, no-regret learning in finite N-
player games. A well-known result in the field states that the empirical frequencies of play …

In this work, we propose introduce a variant of online stochastic gradient descent and prove
it converges to Nash equilibria and simultaneously it has sublinear regret for the class of …

Many multi-agent systems with strategic interactions have their desired functionality
encoded as the Nash equilibrium of a game, eg machine learning architectures such as …

Auctions are modeled as Bayesian games with continuous type and action spaces.
Determining equilibria in auction games is computationally hard in general, and no exact …

In this paper, we introduce a class of learning dynamics for general quantum games, that we
call" follow the quantum regularized leader"(FTQL), in reference to the classical" follow the …