Abstract Counterfactual Regret Minimization (CFR) is the leading algorithm for solving large
imperfect-information games. It converges to an equilibrium by iteratively traversing the …

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 …

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

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 …

Unlike perfect information games, where all elements are known to every player, imperfect
information games emulate the real-world complexities of decision-making under uncertain …

Opponent modeling is the ability to use prior knowledge and observations in order to predict
the behavior of an opponent. This survey presents a comprehensive overview of existing …

While extensive-form games (EFGs) can be converted into normal-form games (NFGs),
doing so comes at the cost of an exponential blowup of the strategy space. So, progress on …

Regret-based algorithms are highly efficient at finding approximate Nash equilibria in
sequential games such as poker games. However, most regret-based algorithms, including …

Blackwell approachability is a framework for reasoning about repeated games with vector-
valued payoffs. We introduce predictive Blackwell approachability, where an estimate of the …

Coordinate descent methods are popular in machine learning and optimization for their
simple sparse updates and excellent practical performance. In the context of large-scale …