Thore Graepel
Thore Graepel
Research Scientist, Google DeepMind, and Professor of Computer Science, UCL
Verified email at
Cited by
Cited by
Mastering the game of Go with deep neural networks and tree search
D Silver, A Huang, CJ Maddison, A Guez, L Sifre, G Van Den Driessche, ...
nature 529 (7587), 484, 2016
Mastering the game of go without human knowledge
D Silver, J Schrittwieser, K Simonyan, I Antonoglou, A Huang, A Guez, ...
Nature 550 (7676), 354-359, 2017
Private traits and attributes are predictable from digital records of human behavior
M Kosinski, D Stillwell, T Graepel
Proceedings of the national academy of sciences 110 (15), 5802-5805, 2013
Large margin rank boundaries for ordinal regression
R Herbrich
Advances in large margin classifiers, 115-132, 2000
TrueSkill™: a Bayesian skill rating system
R Herbrich, T Minka, T Graepel
Advances in neural information processing systems, 569-576, 2007
Mastering chess and shogi by self-play with a general reinforcement learning algorithm
D Silver, T Hubert, J Schrittwieser, I Antonoglou, M Lai, A Guez, M Lanctot, ...
arXiv preprint arXiv:1712.01815, 2017
A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play
D Silver, T Hubert, J Schrittwieser, I Antonoglou, M Lai, A Guez, M Lanctot, ...
Science 362 (6419), 1140-1144, 2018
Web-scale bayesian click-through rate prediction for sponsored search advertising in microsoft's bing search engine
T Graepel, JQ Candela, T Borchert, R Herbrich
Omnipress 27, 13-20, 2010
Support vector learning for ordinal regression
R Herbrich, T Graepel, K Obermayer
IET Digital Library, 1999
Personality and patterns of Facebook usage
Y Bachrach, M Kosinski, T Graepel, P Kohli, D Stillwell
Proceedings of the 4th annual ACM web science conference, 24-32, 2012
ML confidential: Machine learning on encrypted data
T Graepel, K Lauter, M Naehrig
International Conference on Information Security and Cryptology, 1-21, 2012
Matchbox: large scale online bayesian recommendations
DH Stern, R Herbrich, T Graepel
Proceedings of the 18th international conference on World wide web, 111-120, 2009
Bayes point machines
R Herbrich, T Graepel, C Campbell
Journal of Machine Learning Research 1 (Aug), 245-279, 2001
Multi-agent reinforcement learning in sequential social dilemmas
JZ Leibo, V Zambaldi, M Lanctot, J Marecki, T Graepel
arXiv preprint arXiv:1702.03037, 2017
Generalization bounds for the area under the ROC curve
S Agarwal, T Graepel, R Herbrich, S Har-Peled, D Roth
Journal of Machine Learning Research 6 (Apr), 393-425, 2005
Manifestations of user personality in website choice and behaviour on online social networks
M Kosinski, Y Bachrach, P Kohli, D Stillwell, T Graepel
Machine learning 95 (3), 357-380, 2014
How to grade a test without knowing the answers---a Bayesian graphical model for adaptive crowdsourcing and aptitude testing
Y Bachrach, T Graepel, T Minka, J Guiver
arXiv preprint arXiv:1206.6386, 2012
Classification on pairwise proximity data
T Graepel, R Herbrich, P Bollmann-Sdorra, K Obermayer
Advances in neural information processing systems, 438-444, 1999
Human-level performance in 3D multiplayer games with population-based reinforcement learning
M Jaderberg, WM Czarnecki, I Dunning, L Marris, G Lever, AG Castaneda, ...
Science 364 (6443), 859-865, 2019
Gaussian process regression: Active data selection and test point rejection
S Seo, M Wallat, T Graepel, K Obermayer
Mustererkennung 2000, 27-34, 2000
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