| 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), 484-489, 2016 | 21131 | 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 | 12011 | 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 | 5184 | 2018 |
| 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 | 4026 | 2013 |
| Mastering atari, go, chess and shogi by planning with a learned model J Schrittwieser, I Antonoglou, T Hubert, K Simonyan, L Sifre, S Schmitt, ... Nature 588 (7839), 604-609, 2020 | 2786 | 2020 |
| 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 | 2698 | 2017 |
| Value-decomposition networks for cooperative multi-agent learning P Sunehag, G Lever, A Gruslys, WM Czarnecki, V Zambaldi, M Jaderberg, ... arXiv preprint arXiv:1706.05296, 2017 | 2104 | 2017 |
| Large margin rank boundaries for ordinal regression R Herbrich, T Graepel, K Obermayer | 1466 | 2000 |
| TrueSkill™: a Bayesian skill rating system R Herbrich, T Minka, T Graepel Advances in neural information processing systems 19, 2006 | 1152 | 2006 |
| Multi-agent reinforcement learning in sequential social dilemmas JZ Leibo, V Zambaldi, M Lanctot, J Marecki, T Graepel arXiv preprint arXiv:1702.03037, 2017 | 973 | 2017 |
| 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 | 886 | 2019 |
| A unified game-theoretic approach to multiagent reinforcement learning M Lanctot, V Zambaldi, A Gruslys, A Lazaridou, K Tuyls, J Pérolat, D Silver, ... Advances in neural information processing systems 30, 2017 | 836 | 2017 |
| 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 | 765 | 2010 |
| 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 | 674 | 2012 |
| ML confidential: Machine learning on encrypted data T Graepel, K Lauter, M Naehrig International conference on information security and cryptology, 1-21, 2012 | 662 | 2012 |
| Support vector learning for ordinal regression R Herbrich, T Graepel, K Obermayer 1999 Ninth International Conference on Artificial Neural Networks ICANN 99 …, 1999 | 592 | 1999 |
| 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, 357-380, 2014 | 470 | 2014 |
| 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 | 372 | 2009 |
| Bayes point machines R Herbrich, T Graepel, C Campbell Journal of Machine Learning Research 1 (Aug), 245-279, 2001 | 339 | 2001 |
| The mechanics of n-player differentiable games D Balduzzi, S Racaniere, J Martens, J Foerster, K Tuyls, T Graepel International Conference on Machine Learning, 354-363, 2018 | 337 | 2018 |
Ralf HerbrichHasso Plattner InstituteEmail confirmado em hpi.de
