49 citations · 108 across the 34 of their papers we have counts for
5 papers · 1 filter
Game Theoretic Rating in N-player general-sum games with Equilibria
Luke Marris, Marc Lanctot, Ian Gemp +5
Rating strategies in a game is an important area of research in game theory and artificial intelligence, and can be applied to any real-world competitive or cooperative setting. Tr…
Turbocharging Solution Concepts: Solving NEs, CEs and CCEs with Neural Equilibrium Solvers
Luke Marris, Ian Gemp, Thomas Anthony +3
Solution concepts such as Nash Equilibria, Correlated Equilibria, and Coarse Correlated Equilibria are useful components for many multiagent machine learning algorithms. Unfortunat…
Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments
Ian Gemp, Thomas Anthony, Yoram Bachrach +24
The Game Theory & Multi-Agent team at DeepMind studies several aspects of multi-agent learning ranging from computing approximations to fundamental concepts in game theory to simul…
Stochastic Parallelizable Eigengap Dilation for Large Graph Clustering
Elise van der Pol, Ian Gemp, Yoram Bachrach +1
Large graphs commonly appear in social networks, knowledge graphs, recommender systems, life sciences, and decision making problems. Summarizing large graphs by their high level pr…
The Symmetric Generalized Eigenvalue Problem as a Nash Equilibrium
Ian Gemp, Charlie Chen, Brian McWilliams
The symmetric generalized eigenvalue problem (SGEP) is a fundamental concept in numerical linear algebra. It captures the solution of many classical machine learning problems such…