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Kate Larson

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Liquid Democracy for Low-Cost Ensemble Pruning

Jan 30, 2024
Ben Armstrong, Kate Larson

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Evaluating Agents using Social Choice Theory

Dec 07, 2023
Marc Lanctot, Kate Larson, Yoram Bachrach, Luke Marris, Zun Li, Avishkar Bhoopchand, Thomas Anthony, Brian Tanner, Anna Koop

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Towards a Better Understanding of Learning with Multiagent Teams

Jun 28, 2023
David Radke, Kate Larson, Tim Brecht, Kyle Tilbury

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Revealed Multi-Objective Utility Aggregation in Human Driving

Mar 13, 2023
Atrisha Sarkar, Kate Larson, Krzysztof Czarnecki

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Combining Tree-Search, Generative Models, and Nash Bargaining Concepts in Game-Theoretic Reinforcement Learning

Feb 01, 2023
Zun Li, Marc Lanctot, Kevin R. McKee, Luke Marris, Ian Gemp, Daniel Hennes, Paul Muller, Kate Larson, Yoram Bachrach, Michael P. Wellman

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Learning from Multiple Independent Advisors in Multi-agent Reinforcement Learning

Jan 26, 2023
Sriram Ganapathi Subramanian, Matthew E. Taylor, Kate Larson, Mark Crowley

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Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments

Sep 22, 2022
Ian Gemp, Thomas Anthony, Yoram Bachrach, Avishkar Bhoopchand, Kalesha Bullard, Jerome Connor, Vibhavari Dasagi, Bart De Vylder, Edgar Duenez-Guzman, Romuald Elie, Richard Everett, Daniel Hennes, Edward Hughes, Mina Khan, Marc Lanctot, Kate Larson, Guy Lever, Siqi Liu, Luke Marris, Kevin R. McKee, Paul Muller, Julien Perolat, Florian Strub, Andrea Tacchetti, Eugene Tarassov, Zhe Wang, Karl Tuyls

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Exploring the Benefits of Teams in Multiagent Learning

May 04, 2022
David Radke, Kate Larson, Tim Brecht

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The Importance of Credo in Multiagent Learning

Apr 15, 2022
David Radke, Kate Larson, Tim Brecht

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Multi-Agent Advisor Q-Learning

Nov 08, 2021
Sriram Ganapathi Subramanian, Matthew E. Taylor, Kate Larson, Mark Crowley

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