Neil Burch

Senior Software Developer

University of Alberta, Edmonton, AB, Canada

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Papers48 papers

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Sound Search in Imperfect Information Games

Šustr Michal, Schmid Martin,Moravčík Matej,Burch Neil,Lanctot Marc,Bowling Michael

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Variance Reduction in Monte Carlo Regret Minimization for Extensive Games using Baselines

Martin Schmid,Matej Moravcik,Neil Burch,Marc Lanctot, Rudolf Kadlec,Michael Bowling

national conference on artificial intelligence, (2019)

Bibtex

Revisiting CFR+ and Alternating Updates

Neil Burch,Matej Moravcik,Martin Schmid

Journal of Artificial Intelligence Research, (2019)

Cited by11EIBibtex

The Hanabi Challenge: A New Frontier for AI Research

Nolan Bard,Jakob N. Foerster,Sarath Chandar,Neil Burch,Marc Lanctot,Francis Song, Emilio Parisotto,Vincent Dumoulin, Subhodeep Moitra,Edward Hughes,Iain Dunning, Shibl Mourad

Artificial Intelligence, (2019): 103216

Cited by39EIBibtex

Variance Reduction in Monte Carlo Counterfactual Regret Minimization (VR-MCCFR) for Extensive Form Games using Baselines

Martin Schmid,Neil Burch,Marc Lanctot,Matej Moravcik,Rudolf Kadlec,Michael Bowling

national conference on artificial intelligence, (2019)

Cited by11EIBibtex

Rethinking Formal Models of Partially Observable Multiagent Decision Making

Vojtech Kovarík,Martin Schmid,Neil Burch,Michael Bowling, Viliam Lisý

CoRR, (2019)

EIBibtex

Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning

Jakob N. Foerster,Francis Song,Edward Hughes,Neil Burch,Iain Dunning,Shimon Whiteson,Matthew Botvinick,Michael Bowling

international conference on machine learning, (2018)

Cited by34EIBibtex

AIVAT: A New Variance Reduction Technique for Agent Evaluation in Imperfect Information Games

Neil Burch, Martin Schmid, Matej Moravcik, Dustin Morill,Michael Bowling

national conference on artificial intelligence, pp.949-956, (2018)

Cited by6Bibtex

AIVAT: A New Variance Reduction Technique for Agent Evaluation in Imperfect Information Games

Neil Burch,Martin Schmid,Matej Moravcik,Michael H. Bowling

AAAI, (2018)

Cited by2EIBibtex

Heads-up limit hold'em poker is solved

Michael H. Bowling,Neil Burch,Michael Johanson,Oskari Tammelin

Communications of the ACM, no. 11 (2017): 81-88

Cited by12EIBibtex

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

Matej Moravčík, Martin Schmid,Neil Burch,Viliam Lisý,Dustin Morrill,Nolan Bard,Trevor Davis,Kevin Waugh,Michael Johanson,Michael Bowling

science, no. 6337 (2017): 508-513

Cited by166WOSSCIENCEBibtex

Solving heads-up limit Texas Hold'em

Oskari Tammelin,Neil Burch,Michael Johanson,Michael Bowling

IJCAI, (2015)

Cited by93EIBibtex

Heads-up limit hold’em poker is solved

Michael Bowling,Neil Burch,Michael Johanson,Oskari Tammelin

Commun. ACM, no. 6218 (2015): 145-149

Cited by208WOSSCIENCEEIBibtex

Solving Imperfect Information Games Using Decomposition

Neil Burch,Michael Johanson,Michael Bowling

AAAI, pp.602-608, (2014)

Cited by52EIBibtex

CFR-D: Solving Imperfect Information Games Using Decomposition

Neil Burch,Michael Bowling

CoRR, (2013)

Cited by1EIBibtex

Evaluating state-space abstractions in extensive-form games

Michael Johanson,Neil Burch,Richard Valenzano,Michael Bowling

AAMAS, pp.271-278, (2013)

Cited by71EIBibtex

Online implicit agent modelling

Nolan Bard,Michael Johanson,Neil Burch,Michael Bowling

AAMAS, pp.255-262, (2013)

Cited by37EIBibtex

Rating players in games with real-valued outcomes

Christopher Archibald,Neil Burch,Michael Bowling,Matthew Rutherford

AAMAS, pp.1307-1308, (2013)

Cited by1EIBibtex

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

Marc Lanctot,Richard G. Gibson,Neil Burch,Michael Bowling

ICML, (2012)

EIBibtex

Efficient Monte Carlo Counterfactual Regret Minimization in Games with Many Player Actions

Richard G. Gibson,Neil Burch,Marc Lanctot,Duane Szafron

NIPS, pp.1889-1897, (2012)

Cited by18EIBibtex

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Neil BurchBinding

Papers48 papers

Neil Burch