7 citations · 14 across the 7 of their papers we have counts for
8 papers
Algorithmic Asymmetry in Zero-Sum Games: Unilateral Recovery of Fast Convergence Against a Slow Opponent
James P. Bailey, Soham Das
Learning dynamics in zero-sum games are typically analyzed under algorithmic symmetry: both agents use the same update rule, or methods from a common algorithmic family. This is at…
Implicit Midpoint Gradient Descent: Fast and Learning rate free convergence for Zero-Sum Games
Gaoqi Xue, James P. Bailey
We study unconstrained bilinear zero-sum games, a fundamental model in online learning, adversarial optimization, and multi-agent decision-making. We introduce the implicit midpoin…
Time-Average Convergence in a Generalization of Multiagent Zero-Sum Games
James P. Bailey
We introduce a generalization of zero-sum network multiagent matrix games and prove that alternating gradient descent converges to the set of Nash equilibria at rate for t…
Stochastic Multiplicative Weights Updates in Zero-Sum Games
James P. Bailey, Sai Ganesh Nagarajan, Georgios Piliouras
We study agents competing against each other in a repeated network zero-sum game while applying the multiplicative weights update (MWU) algorithm with fixed learning rates. In our…
Conditions for Stability in Strategic Matching
James P. Bailey, Craig A. Tovey
We consider the stability of matchings when individuals strategically submit preference information to a publicly known algorithm. Most pure Nash equilibria of the ensuing game yie…
Finite Regret and Cycles with Fixed Step-Size via Alternating Gradient Descent-Ascent
James P. Bailey, Gauthier Gidel, Georgios Piliouras
Gradient descent is arguably one of the most popular online optimization methods with a wide array of applications. However, the standard implementation where agents simultaneously…