7 citations · 14 across the 5 of their papers we have counts for
6 papers · 1 filter
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…
Fast and Furious Learning in Zero-Sum Games: Vanishing Regret with Non-Vanishing Step Sizes
James P. Bailey, Georgios Piliouras
We show for the first time, to our knowledge, that it is possible to reconcile in online learning in zero-sum games two seemingly contradictory objectives: vanishing time-average r…
Multi-Agent Learning in Network Zero-Sum Games is a Hamiltonian System
James P. Bailey, Georgios Piliouras
Zero-sum games are natural, if informal, analogues of closed physical systems where no energy/utility can enter or exit. This analogy can be extended even further if we consider ze…