Thore Graepel

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Bendraautoriai

Ralf HerbrichHasso Plattner InstitutePatvirtintas el. paštas hpi.de
Marc LanctotResearch Scientist, DeepMindPatvirtintas el. paštas google.com
David SilverDeepMind, UCLPatvirtintas el. paštas google.com
Laurent SifreGoogle DeepMindPatvirtintas el. paštas polytechnique.edu
Arthur GuezGoogle DeepMindPatvirtintas el. paštas google.com
Yoram BachrachDeepMindPatvirtintas el. paštas google.com
Timothy P. LillicrapSenior Research Scientist, Google DeepMindPatvirtintas el. paštas google.com
Joel Z LeiboResearch scientistPatvirtintas el. paštas google.com
Klaus ObermayerProfessor, Fakultät IV - Electrical Engineering and Computer Science, Technische Universität BerlinPatvirtintas el. paštas tu-berlin.de
Karl TuylsResearch Scientist, Google DeepMind and Professor of computer science, University of LiverpoolPatvirtintas el. paštas google.com
Chris J. MaddisonUniversity of TorontoPatvirtintas el. paštas cs.toronto.edu
Aja HuangDeepMindPatvirtintas el. paštas google.com
Michal KosinskiStanford UniversityPatvirtintas el. paštas stanford.edu
Karen SimonyanCo-Founder and Chief Scientist at Inflection AIPatvirtintas el. paštas inflection.ai
Thomas HubertGoogle DeepmindPatvirtintas el. paštas google.com
David StillwellCambridge UniversityPatvirtintas el. paštas cam.ac.uk
koray kavukcuogluDeepMindPatvirtintas el. paštas kavukcuoglu.org
Matthew LaiDeepMindPatvirtintas el. paštas matthewlai.ca
julien perolatDeepMindPatvirtintas el. paštas google.com
Pushmeet KohliDeepMindPatvirtintas el. paštas google.com

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Thore Graepel

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

Patvirtintas el. paštas ucl.ac.uk - Pagrindinis puslapis

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


Pavadinimas Rūšiuoti pagal šaltinius Rūšiuoti pagal metus Rūšiuoti pagal pavadinimą	Cituota Cituota	Metai
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	15896	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	8966	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	3430	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	3266	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	1737	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	1520	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	1085	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	741	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	569	2017
Support vector learning for ordinal regression R Herbrich, T Graepel, K Obermayer IET Digital Library, 1999	565	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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