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Ryann Sim

7 accepted papers

2025

Certifying Concavity and Monotonicity in Games via Sum-of-Squares Hierarchies

NeurIPS 2025poster

Concavity and its refinements underpin tractability in multiplayer games, where players independently choose actions to maximize their own payoffs which depend on other players’ actions. In *concave* games, where players' strategy sets are compact and convex, and their payoffs are concave in their o…

Cited by 0SourceScholar
2025

Optimism Without Regularization: Constant Regret in Zero-Sum Games

NeurIPS 2025poster

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 bounded stepsize parameter -- obtains constant regret in this setting, we show for the first time that similar, optima…

Cited by 0SourceScholar
2022

Beyond Time-Average Convergence: Near-Optimal Uncoupled Online Learning via Clairvoyant Multiplicative Weights Update

NeurIPS 2022accept

In this paper we provide a novel and simple algorithm, Clairvoyant Multiplicative Weights Updates (CMWU), for convergence to \textit{Coarse Correlated Equilibria} (CCE) in general games. CMWU effectively corresponds to the standard MWU algorithm but where all agents, when updating their mixed strate…

Cited by 5SourcePDFScholar
2022

Matrix Multiplicative Weights Updates in Quantum Zero-Sum Games: Conservation Laws & Recurrence

NeurIPS 2022accept

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 learning algorithms for classical games into the quantum realm. In this paper, we focus on learning in quantum zero-sum gam…

Cited by 10SourcePDFScholar
2021

Evolutionary Game Theory Squared: Evolving Agents in Endogenously Evolving Zero-Sum Games

AAAI 2021technical

The predominant paradigm in evolutionary game theory and more generally online learning in games is based on a clear distinction between a population of dynamic agents that interact given a fixed, static game. In this paper, we move away from the artificial divide between dynamic agents and static g…

2021

Online Learning in Periodic Zero-Sum Games

NeurIPS 2021poster

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 on this theorem to show that online no-regret dynamics converge to an equilibrium in a time-average sense in zero-sum g…

Cited by 12SourcePDFScholar