4 citations · 7 across the 11 of their papers we have counts for
13 papers
Equilibrium Computation in Extensive-Form Games with Stochastic Action Sets
Thomas Schwarz, Jiaru Li, Ryann Sim +1
Extensive-form games (EFGs) are a standard model for sequential decision-making in games. A fundamental and typically implicit assumption in EFGs is that players always have access…
Solving Imperfect-Recall Games via Sum-of-Squares Optimization
Rui Zheng, Ryann Sim, Antonios Varvitsiotis
Extensive-form games (EFGs) provide a powerful framework for modeling sequential decision making, capturing strategic interaction under imperfect information, chance events, and te…
Computing Equilibria in Games with Stochastic Action Sets
Thomas Schwarz, Jiaru Li, Ryann Sim +1
The study of learning in games typically assumes that each player always has access to all of their actions. However, in many practical scenarios, players' available actions might…
Certifying Concavity and Monotonicity in Games via Sum-of-Squares Hierarchies
Vincent Leon, Iosif Sakos, Ryann Sim +1
Concavity and its refinements underpin tractability in multiplayer games, where players independently choose actions to maximize their own payoffs which depend on other players' ac…
Fast and Furious Symmetric Learning in Zero-Sum Games: Gradient Descent as Fictitious Play
John Lazarsfeld, Georgios Piliouras, Ryann Sim +1
This paper investigates the sublinear regret guarantees of two non-no-regret algorithms in zero-sum games: Fictitious Play, and Online Gradient Descent with constant stepsizes. In…
Optimism Without Regularization: Constant Regret in Zero-Sum Games
John Lazarsfeld, Georgios Piliouras, Ryann Sim +1
This paper studies the optimistic variant of Fictitious Play for learning in two-player zero-sum games. While it is known that Optimistic FTRL -- a regularized algorithm with a bou…
No-Regret Learning and Equilibrium Computation in Quantum Games
Wayne Lin, Georgios Piliouras, Ryann Sim +1
As quantum processors advance, the emergence of large-scale decentralized systems involving interacting quantum-enabled agents is on the horizon. Recent research efforts have explo…
Learning in Quantum Common-Interest Games and the Separability Problem
Wayne Lin, Georgios Piliouras, Ryann Sim +1
Learning in games has emerged as a powerful tool for machine learning with numerous applications. Quantum games model interactions between strategic players who have access to quan…
Min-Max Optimization Made Simple: Approximating the Proximal Point Method via Contraction Maps
Volkan Cevher, Georgios Piliouras, Ryann Sim +1
In this paper we present a first-order method that admits near-optimal convergence rates for convex/concave min-max problems while requiring a simple and intuitive analysis. Simila…
Matrix Multiplicative Weights Updates in Quantum Zero-Sum Games: Conservation Laws & Recurrence
Rahul Jain, Georgios Piliouras, Ryann Sim
Recent advances in quantum computing and in particular, the introduction of quantum GANs, have led to increased interest in quantum zero-sum game theory, extending the scope of lea…
Fast Convergence of Optimistic Gradient Ascent in Network Zero-Sum Extensive Form Games
Georgios Piliouras, Lillian Ratliff, Ryann Sim +1
The study of learning in games has thus far focused primarily on normal form games. In contrast, our understanding of learning in extensive form games (EFGs) and particularly in EF…
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…
Beyond Time-Average Convergence: Near-Optimal Uncoupled Online Learning via Clairvoyant Multiplicative Weights Update
Georgios Piliouras, Ryann Sim, Stratis Skoulakis
In this paper, we provide a novel and simple algorithm, Clairvoyant Multiplicative Weights Updates (CMWU) for regret minimization in general games. CMWU effectively corresponds to…