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 …

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 …

While classic work in convex-concave min-max optimization relies on average-iterate
convergence results, the emergence of nonconvex applications such as training Generative …

We study the performance of optimistic regret-minimization algorithms for both minimizing
regret in, and computing Nash equilibria of, zero-sum extensive-form games. In order to …

While classic work in convex-concave min-max optimization relies on average-iterate
convergence results, the emergence of nonconvex applications such as training Generative …

Markov decision processes (MDPs) are known to be sensitive to parameter specification.
Distributionally robust MDPs alleviate this issue by allowing for\textit {ambiguity sets} which …

Abstract Robust Markov Decision Processes (MDPs) are a powerful framework for modeling
sequential decision making problems with model uncertainty. This paper proposes the first …

In this paper, we propose a new algorithm for solving convex-concave saddle-point
problems using regret minimization in the repeated game framework. To do so, we introduce …