In many practical applications, such as poker, chess, drug interdiction, cybersecurity, and
national defense, players often have adversarial stances, ie, the selfish actions of each …

Abstract The Past Extragradient (PEG)[Popov, 1980] method, also known as the Optimistic
Gradient method, has known a recent gain in interest in the optimization community with the …

We introduce a new approach for computing optimal equilibria via learning in games. It
applies to extensive-form settings with any number of players, including mechanism design …

Most of the literature on learning in games has focused on the restrictive setting where the
underlying repeated game does not change over time. Much less is known about the …

Algorithms for min-max optimization and variational inequalities are often studied under
monotonicity assumptions. Motivated by non-monotone machine learning applications, we …

When applied to question answering and other text generation tasks, language models
(LMs) may be queried generatively (by sampling answers from their output distribution) or …

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 …

The works of (Daskalakis et al., 2009, 2022; ** et al., 2022; Deng et al., 2023) indicate that
computing Nash equilibria in multi-player Markov games is a computationally hard task. This …

We study a new class of Markov games,\textit {(multi-player) zero-sum Markov Games} with
{\it Networked separable interactions}(zero-sum NMGs), to model the local interaction …

We study the problem of characterizing optimal learning algorithms for playing repeated
games against an adversary with unknown payoffs. In this problem, the first player (called …