78 citations · 333 across the 57 of their papers we have counts for
6 papers · 1 filter
Online Learning in Periodic Zero-Sum Games
Tanner Fiez, Ryann Sim, Stratis Skoulakis +2
A seminal result in game theory is von Neumann's minmax theorem, which states that zero-sum games admit an essentially unique equilibrium solution. Classical learning results build…
Improved Rates for Derivative Free Gradient Play in Strongly Monotone Games
Dmitriy Drusvyatskiy, Maryam Fazel, Lillian J Ratliff
The influential work of Bravo et al. 2018 shows that derivative free play in strongly monotone games has complexity , where is the target accura…
Stackelberg Actor-Critic: Game-Theoretic Reinforcement Learning Algorithms
Liyuan Zheng, Tanner Fiez, Zane Alumbaugh +2
The hierarchical interaction between the actor and critic in actor-critic based reinforcement learning algorithms naturally lends itself to a game-theoretic interpretation. We adop…
Approximate Regions of Attraction in Learning with Decision-Dependent Distributions
Roy Dong, Heling Zhang, Lillian J. Ratliff
As data-driven methods are deployed in real-world settings, the processes that generate the observed data will often react to the decisions of the learner. For example, a data sour…
Minimax Optimization with Smooth Algorithmic Adversaries
Tanner Fiez, Chi Jin, Praneeth Netrapalli +1
This paper considers minimax optimization in the challenging setting where can be both nonconvex in and nonconcave in . Though such optimization…
Zeroth-Order Methods for Convex-Concave Minmax Problems: Applications to Decision-Dependent Risk Minimization
Chinmay Maheshwari, Chih-Yuan Chiu, Eric Mazumdar +2
Min-max optimization is emerging as a key framework for analyzing problems of robustness to strategically and adversarially generated data. We propose a random reshuffling-based gr…