Thore Graepel

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	Wszystkie	Od 2018
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Współautorzy

Ralf HerbrichHasso Plattner InstituteZweryfikowany adres z hpi.de
Marc LanctotResearch Scientist, DeepMindZweryfikowany adres z google.com
David SilverDeepMind, UCLZweryfikowany adres z google.com
Laurent SifreGoogle DeepMindZweryfikowany adres z polytechnique.edu
Arthur GuezGoogle DeepMindZweryfikowany adres z google.com
Yoram BachrachDeepMindZweryfikowany adres z google.com
Timothy P. LillicrapSenior Research Scientist, Google DeepMindZweryfikowany adres z google.com
Joel Z LeiboResearch scientistZweryfikowany adres z google.com
Klaus ObermayerProfessor, Fakultät IV - Electrical Engineering and Computer Science, Technische Universität BerlinZweryfikowany adres z tu-berlin.de
Karl TuylsResearch Scientist, Google DeepMind and Professor of computer science, University of LiverpoolZweryfikowany adres z google.com
Chris J. MaddisonUniversity of TorontoZweryfikowany adres z cs.toronto.edu
Aja HuangDeepMindZweryfikowany adres z google.com
Michal KosinskiStanford UniversityZweryfikowany adres z stanford.edu
Karen SimonyanCo-Founder and Chief Scientist at Inflection AIZweryfikowany adres z inflection.ai
Thomas HubertGoogle DeepmindZweryfikowany adres z google.com
David StillwellCambridge UniversityZweryfikowany adres z cam.ac.uk
koray kavukcuogluDeepMindZweryfikowany adres z kavukcuoglu.org
Matthew LaiDeepMindZweryfikowany adres z matthewlai.ca
julien perolatDeepMindZweryfikowany adres z google.com
Pushmeet KohliDeepMindZweryfikowany adres z google.com

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Thore Graepel

Global Lead Computational Science, AI & ML at Altos Labs and Chair of Machine Learning, UCL

Zweryfikowany adres z ucl.ac.uk - Strona główna

Machine Learning Reinforcement Learning Deep Learning Multi-Agent Learning Computational Biology


Tytuł Sortuj wg cytatów Sortuj wg roku Sortuj wg tytułu	Cytowane przez Cytowane przez	Rok
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	15955	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	8989	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	3443	2013
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	3285	2018
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	1740	2017
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	1534	2020
Large margin rank boundaries for ordinal regression R Herbrich Advances in large margin classifiers, 115-132, 2000	1414	2000
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	1089	2017
TrueSkill™: a Bayesian skill rating system R Herbrich, T Minka, T Graepel Advances in neural information processing systems 19, 2006	940	2006
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	743	2019
Multi-agent reinforcement learning in sequential social dilemmas JZ Leibo, V Zambaldi, M Lanctot, J Marecki, T Graepel arXiv preprint arXiv:1702.03037, 2017	698	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	680	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	606	2012
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	576	2017
Support vector learning for ordinal regression R Herbrich, T Graepel, K Obermayer IET Digital Library, 1999	566	1999
ML confidential: Machine learning on encrypted data T Graepel, K Lauter, M Naehrig Information Security and Cryptology–ICISC 2012: 15th International …, 2013	557	2013
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	399	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	356	2009
Bayes point machines R Herbrich, T Graepel, C Campbell Journal of Machine Learning Research 1 (Aug), 245-279, 2001	322	2001
Generalization Bounds for the Area Under the ROC Curve. S Agarwal, T Graepel, R Herbrich, S Har-Peled, D Roth, MI Jordan Journal of Machine Learning Research 6 (4), 2005	290	2005

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