T. Graepel

Most-cited papers

Mastering the game of Go with deep neural networks and tree search
David Silver, A. Huang, Chris J. Maddison, A. Guez +16 more
2016Nature19K citations Open access
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Mastering the game of Go without human knowledge
David Silver, J. Schrittwieser, K. Simonyan, I. Antonoglou +13 more
2017Nature11K citations
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A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play
David Silver, T. Hubert, J. Schrittwieser, I. Antonoglou +9 more
2018Science4,283 citations Open access
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Mastering Atari, Go, chess and shogi by planning with a learned model
J. Schrittwieser, I. Antonoglou, T. Hubert, K. Simonyan +8 more
2019Nature2,834 citations Open access
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Private traits and attributes are predictable from digital records of human behavior
Michal Kosinski, D. Stillwell, T. Graepel
2013Proceedings of the National Academy of Sciences of the United States of America2,706 citations Open access
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Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
David Silver, T. Hubert, J. Schrittwieser, I. Antonoglou +9 more
2017arXiv.org2,162 citations
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Value-Decomposition Networks For Cooperative Multi-Agent Learning
P. Sunehag, Guy Lever, A. Gruslys, W. Czarnecki +7 more
2017arXiv.org1,428 citations
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Value-Decomposition Networks For Cooperative Multi-Agent Learning Based On Team Reward
P. Sunehag, Guy Lever, A. Gruslys, W. Czarnecki +7 more
2018Adaptive Agents and Multi-Agent Systems976 citations
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Human-level performance in 3D multiplayer games with population-based reinforcement learning
M. Jaderberg, W. Czarnecki, Iain Dunning, Luke Marris +14 more
2018Science836 citations Open access
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A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning
Marc Lanctot, V. Zambaldi, A. Gruslys, Angeliki Lazaridou +4 more
2017Neural Information Processing Systems783 citations
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Multi-agent Reinforcement Learning in Sequential Social Dilemmas
J. Leibo, V. Zambaldi, Marc Lanctot, J. Marecki +1 more
2017Adaptive Agents and Multi-Agent Systems749 citations
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ML Confidential: Machine Learning on Encrypted Data
T. Graepel, K. Lauter, Michael Naehrig
2012International Conference on Information Security and Cryptology503 citations
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Personality and patterns of Facebook usage
Yoram Bachrach, Michal Kosinski, T. Graepel, Pushmeet Kohli +1 more
2012Web Science Conference354 citations
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The Mechanics of n-Player Differentiable Games
David Balduzzi, S. Racanière, James Martens, Jakob N. Foerster +2 more
2018International Conference on Machine Learning331 citations
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Cooperative AI: machines must learn to find common ground
Allan Dafoe, Yoram Bachrach, Gillian K. Hadfield, Eric Horvitz +2 more
2021Nature303 citations
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Open Problems in Cooperative AI
Allan Dafoe, Edward Hughes, Yoram Bachrach, Tantum Collins +4 more
2020arXiv.org298 citations
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Inequity aversion improves cooperation in intertemporal social dilemmas
Edward Hughes, J. Leibo, Matthew Phillips, K. Tuyls +8 more
2018Neural Information Processing Systems287 citations
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Manifestations of user personality in website choice and behaviour on online social networks
Michal Kosinski, Yoram Bachrach, Pushmeet Kohli, D. Stillwell +1 more
2013Machine-mediated learning260 citations Open access
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A multi-agent reinforcement learning model of common-pool resource appropriation
J. Pérolat, J. Leibo, V. Zambaldi, Charlie Beattie +2 more
2017Neural Information Processing Systems240 citations
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How To Grade a Test Without Knowing the Answers - A Bayesian Graphical Model for Adaptive Crowdsourcing and Aptitude Testing
Yoram Bachrach, T. Graepel, T. Minka, J. Guiver
2012International Conference on Machine Learning214 citations
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Open-ended Learning in Symmetric Zero-sum Games
David Balduzzi, M. Garnelo, Yoram Bachrach, W. Czarnecki +3 more
2019International Conference on Machine Learning208 citations
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SIGMa: simple greedy matching for aligning large knowledge bases
Simon Lacoste-Julien, Konstantina Palla, A. Davies, Gjergji Kasneci +2 more
2012Knowledge Discovery and Data Mining206 citations Open access
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From Motor Control to Team Play in Simulated Humanoid Football
Siqi Liu, Guy Lever, Zhe Wang, J. Merel +18 more
2021Sci. Robotics171 citations Open access
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Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot
J. Leibo, Edgar A. Duéñez-Guzmán, A. Vezhnevets, J. Agapiou +6 more
2021International Conference on Machine Learning167 citations
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Emergent Coordination Through Competition
Siqi Liu, Guy Lever, J. Merel, S. Tunyasuvunakool +2 more
2019International Conference on Learning Representations160 citations
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Inferring the demographics of search users: social data meets search queries
Bin Bi, Milad Shokouhi, Michal Kosinski, T. Graepel
2013The Web Conference130 citations
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Autocurricula and the Emergence of Innovation from Social Interaction: A Manifesto for Multi-Agent Intelligence Research
J. Leibo, Edward Hughes, Marc Lanctot, T. Graepel
2019arXiv.org125 citations
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Re-evaluating evaluation
David Balduzzi, K. Tuyls, J. Pérolat, T. Graepel
2018Neural Information Processing Systems124 citations
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A Generalized Training Approach for Multiagent Learning
Paul Muller, Shayegan Omidshafiei, Mark Rowland, K. Tuyls +11 more
2019International Conference on Learning Representations118 citations
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Game Plan: What AI can do for Football, and What Football can do for AI
K. Tuyls, Shayegan Omidshafiei, Paul Muller, Zhe Wang +32 more
2020Journal of Artificial Intelligence Research115 citations
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Differentiable Game Mechanics
Alistair Letcher, David Balduzzi, S. Racanière, James Martens +3 more
2019Journal of machine learning research98 citations
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PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis
Yan Wu, E. Wershof, Sebastian M. Schmon, Marcel Nassar +6 more
2024Neural Information Processing Systems96 citations
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Biases for Emergent Communication in Multi-agent Reinforcement Learning
Tom Eccles, Yoram Bachrach, Guy Lever, Angeliki Lazaridou +1 more
2019Neural Information Processing Systems94 citations
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Relational Forward Models for Multi-Agent Learning
Andrea Tacchetti, H. F. Song, P. Mediano, V. Zambaldi +5 more
2018International Conference on Learning Representations85 citations
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Crowd IQ: aggregating opinions to boost performance
Yoram Bachrach, T. Graepel, Gjergji Kasneci, Michal Kosinski +1 more
2012Adaptive Agents and Multi-Agent Systems73 citations
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Kernel Topic Models
Philipp Hennig, David H. Stern, R. Herbrich, T. Graepel
2011International Conference on Artificial Intelligence and Statistics68 citations
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Tabular: a schema-driven probabilistic programming language
A. Gordon, T. Graepel, Nicolas Rolland, Claudio V. Russo +2 more
2014ACM-SIGACT Symposium on Principles of Programming Languages67 citations Open access
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Crowd IQ: measuring the intelligence of crowdsourcing platforms
Michal Kosinski, Yoram Bachrach, Gjergji Kasneci, Jurgen Van Gael +1 more
2012Web Science Conference66 citations
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A Generalised Method for Empirical Game Theoretic Analysis
K. Tuyls, J. Pérolat, Marc Lanctot, J. Leibo +1 more
2018Adaptive Agents and Multi-Agent Systems63 citations Open access
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Automated feature generation from structured knowledge
Weiwei Cheng, Gjergji Kasneci, T. Graepel, David H. Stern +1 more
2011International Conference on Information and Knowledge Management61 citations
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EigenGame: PCA as a Nash Equilibrium
I. Gemp, B. McWilliams, Claire Vernade, T. Graepel
2020International Conference on Learning Representations60 citations
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Sociable killers: understanding social relationships in an online first-person shooter game
Yan Xu, Xiang Cao, A. Sellen, R. Herbrich +1 more
2011Conference on Computer Supported Cooperative Work60 citations
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Negotiation and honesty in artificial intelligence methods for the board game of Diplomacy
János Kramár, Tom Eccles, I. Gemp, Andrea Tacchetti +4 more
2022Nature Communications59 citations Open access
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Learning to Play No-Press Diplomacy with Best Response Policy Iteration
Thomas W. Anthony, Tom Eccles, Andrea Tacchetti, J'anos Kram'ar +10 more
2020Neural Information Processing Systems50 citations
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Learning Shared Representations in Multi-task Reinforcement Learning
Diana Borsa, T. Graepel, J. Shawe-Taylor
201648 citations
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Negotiating team formation using deep reinforcement learning
Yoram Bachrach, Richard Everett, Edward Hughes, Angeliki Lazaridou +5 more
2020Artificial Intelligence47 citations Open access
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Multi-Agent Training beyond Zero-Sum with Correlated Equilibrium Meta-Solvers
Luke Marris, Paul Muller, Marc Lanctot, K. Tuyls +1 more
2021International Conference on Machine Learning43 citations
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Malthusian Reinforcement Learning
J. Leibo, J. Pérolat, Edward Hughes, S. Wheelwright +5 more
2018Adaptive Agents and Multi-Agent Systems43 citations Open access
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Personality and Website Choice
Michal Kosinski, D. Stillwell, Pushmeet Kohli, Yoram Bachrach +1 more
201242 citations
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Quality Expectation-Variance Tradeoffs in Crowdsourcing Contests
X. Gao, Yoram Bachrach, P. Key, T. Graepel
2012AAAI Conference on Artificial Intelligence42 citations Open access
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