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Overview

Neil Burch is affiliated with the University of Alberta in Canada and conducts research primarily in the field of Computer Science, with a strong focus on Artificial Intelligence. Their work spans several subfields including Management Science and Operations Research, Economics and Econometrics, Developmental and Educational Psychology, and Computer Vision and Pattern Recognition.

Their research topics include:

  • Reinforcement Learning in Robotics
  • Artificial Intelligence in Games
  • Game Theory and Applications
  • Sports Analytics and Performance
  • Adversarial Robustness in Machine Learning
  • Advanced Bandit Algorithms Research
  • Explainable Artificial Intelligence (XAI)

Neil Burch's recent publications highlight their involvement in multiagent decision making and game theory. Notable papers include:

  • "Mastering the game of Stratego with model-free multiagent reinforcement learning," 2022, Science
  • "Finding Optimal Abstract Strategies in Extensive-Form Games," 2021, Proceedings of the AAAI Conference on Artificial Intelligence
  • "Rethinking formal models of partially observable multiagent decision making," 2021, Artificial Intelligence
  • "From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization," 2020, arXiv (Cornell University)
  • "Generalized Sampling and Variance in Counterfactual Regret Minimization," 2021, Proceedings of the AAAI Conference on Artificial Intelligence

Frequent co-authors in Neil Burch's research include:

  • Marc Lanctot
  • Finbarr Timbers
  • Michael Bowling
  • Edward Lockhart
  • Julien Pérolat

Neil Burch regularly publishes in venues such as:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Proceedings of the International Symposium on Combinatorial Search
  • Zenodo (CERN European Organization for Nuclear Research)
  • Science

Best Publications

  • DeepStack: Expert-level artificial intelligence in heads-up no-limit poker

    Matej Moravčík;Matej Moravčík;Martin Schmid;Martin Schmid;Neil Burch;Viliam Lisý;Viliam Lisý

  • Checkers Is Solved

    Jonathan Schaeffer;Neil Burch;Yngvi Björnsson;Akihiro Kishimoto

  • Heads-up limit hold'em poker is solved

    Michael Bowling;Neil Burch;Michael Johanson;Oskari Tammelin

  • Approximating game-theoretic optimal strategies for full-scale poker

    D. Billings;N. Burch;A. Davidson;R. Holte

  • The Hanabi Challenge: A New Frontier for AI Research

    Nolan Bard;Jakob N. Foerster;Sarath Chandar;Neil Burch

  • Bayes' bluff: opponent modelling in poker

    Finnegan Southey;Michael Bowling;Bryce Larson;Carmelo Piccione

  • Solving heads-up limit Texas Hold'em

    Oskari Tammelin;Neil Burch;Michael Johanson;Michael Bowling

  • Game-Tree search with adaptation in stochastic imperfect-information games

    Darse Billings;Aaron Davidson;Terence Schauenberg;Neil Burch

  • Memory-based heuristics for explicit state spaces

    Nathan R. Sturtevant;Ariel Felner;Max Barrer;Jonathan Schaeffer

  • Evaluating state-space abstractions in extensive-form games

    Michael Johanson;Neil Burch;Richard Valenzano;Michael Bowling

  • Finding optimal abstract strategies in extensive-form games

    Michael Johanson;Nolan Bard;Neil Burch;Michael Bowling

  • Solving imperfect information games using decomposition

    Neil Burch;Michael Johanson;Michael Bowling

  • Solving checkers

    J. Schaeffer;Y. Björnsson;N. Burch;A. Kishimoto

  • No-Regret Learning in Extensive-Form Games with Imperfect Recall

    Marc Lanctot;Richard Gibson;Neil Burch;Martin Zinkevich

  • Block A*: database-driven search with applications in any-angle path-planning

    Peter Yap;Neil Burch;Rob Holte;Jonathan Schaeffer

  • No-Regret Learning in Extensive-Form Games with Imperfect Recall

    Marc Lanctot;Neil Burch;Martin Zinkevich;Michael Bowling

  • Predicting the performance of IDA* using conditional distributions

    Uzi Zahavi;Ariel Felner;Neil Burch;Robert C. Holte

  • Online implicit agent modelling

    Nolan Bard;Michael Johanson;Neil Burch;Michael Bowling

  • Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning

    Jakob N. Foerster;H. Francis Song;Edward Hughes;Neil Burch

  • From Poincar'e Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization

    Julien Perolat;Remi Munos;Jean-Baptiste Lespiau;Shayegan Omidshafiei

Frequent Co-Authors

Michael Bowling
Michael Bowling University of Alberta
Robert C. Holte
Robert C. Holte University of Alberta
Jonathan Schaeffer
Jonathan Schaeffer University of Alberta
Marc Lanctot
Marc Lanctot DeepMind (United Kingdom)
Duane Szafron
Duane Szafron University of Alberta
Ariel Felner
Ariel Felner Ben-Gurion University of the Negev
Jakob Foerster
Jakob Foerster University of Oxford
Shimon Whiteson
Shimon Whiteson University of Oxford
Matthew Botvinick
Matthew Botvinick Yale University
Rémi Munos
Rémi Munos French Institute for Research in Computer Science and Automation - INRIA

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