Rémi Munos

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Michal ValkoLlama @ Meta Paris & Inria & MVA - Ex: Gemini and BYOL @ Google DeepMindEmail yang diverifikasi di meta.com
Mohammad Gheshlaghi AzarCohereEmail yang diverifikasi di cohere.com
Marc G. BellemareReliant AIEmail yang diverifikasi di reliant.ai
Csaba SzepesvariDeepMind & University of AlbertaEmail yang diverifikasi di cs.ualberta.ca
Alessandro LazaricResearch Scientist, Facebook Artificial Intelligence ResearchEmail yang diverifikasi di inria.fr
koray kavukcuogluDeepMindEmail yang diverifikasi di kavukcuoglu.org
Odalric-Ambrym MaillardInria Lille - Nord EuropeEmail yang diverifikasi di inria.fr
Sebastien BubeckOpenAIEmail yang diverifikasi di openai.com
Andrew MooreDean, School of Computer Science, Carnegie MellonEmail yang diverifikasi di cs.cmu.edu
Volodymyr MnihDeepMindEmail yang diverifikasi di cs.toronto.edu
Anna HarutyunyanDeepMindEmail yang diverifikasi di google.com
Marc LanctotResearch Scientist, Google DeepMindEmail yang diverifikasi di google.com
Tom SchaulSenior Staff Scientist, DeepMindEmail yang diverifikasi di nyu.edu
András AntosBudapest University of Technology and EconomicsEmail yang diverifikasi di cs.bme.hu
Hilbert Johan KappenRadboud UniversityEmail yang diverifikasi di science.ru.nl
David SilverDeepMind, UCLEmail yang diverifikasi di google.com
Lucian BusoniuProfessor and Group Lead, Automation Department, Technical University of Cluj-NapocaEmail yang diverifikasi di aut.utcluj.ro
Andre BarretoResearch Scientist, Google DeepMindEmail yang diverifikasi di google.com
Olivier TeytaudfacebookEmail yang diverifikasi di fb.com
Sylvain GellyGoogle Brain ZurichEmail yang diverifikasi di m4x.org

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Rémi Munos

Google DeepMind

Email yang diverifikasi di inria.fr - Beranda

Reinforcement learning RLHF MCTS bandit theory statistical learning


Judul Urutkan menurut kutipan Urutkan menurut tahun Urutkan menurut judul	Dikutip oleh Dikutip oleh	Tahun
Bootstrap your own latent-a new approach to self-supervised learning JB Grill, F Strub, F Altché, C Tallec, P Richemond, E Buchatskaya, ... Advances in neural information processing systems 33, 21271-21284, 2020	6929	2020
A distributional perspective on reinforcement learning MG Bellemare, W Dabney, R Munos International conference on machine learning, 449-458, 2017	1902	2017
Unifying count-based exploration and intrinsic motivation M Bellemare, S Srinivasan, G Ostrovski, T Schaul, D Saxton, R Munos Advances in neural information processing systems 29, 2016	1763	2016
Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures L Espeholt, H Soyer, R Munos, K Simonyan, V Mnih, T Ward, Y Doron, ... International conference on machine learning, 1407-1416, 2018	1721	2018
Learning to reinforcement learn JX Wang, Z Kurth-Nelson, D Tirumala, H Soyer, JZ Leibo, R Munos, ... arXiv preprint arXiv:1611.05763, 2016	1093	2016
Sample efficient actor-critic with experience replay Z Wang, V Bapst, N Heess, V Mnih, R Munos, K Kavukcuoglu, ... arXiv preprint arXiv:1611.01224, 2016	1034	2016
Best arm identification in multi-armed bandits JY Audibert, S Bubeck COLT-23th Conference on learning theory-2010, 13 p., 2010	974	2010
Distributional reinforcement learning with quantile regression W Dabney, M Rowland, M Bellemare, R Munos Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018	897	2018
Minimax regret bounds for reinforcement learning MG Azar, I Osband, R Munos International conference on machine learning, 263-272, 2017	884	2017
Exploration–exploitation tradeoff using variance estimates in multi-armed bandits JY Audibert, R Munos, C Szepesvári Theoretical Computer Science 410 (19), 1876-1902, 2009	800	2009
Thompson sampling: An asymptotically optimal finite-time analysis E Kaufmann, N Korda, R Munos International conference on algorithmic learning theory, 199-213, 2012	795	2012
Count-based exploration with neural density models G Ostrovski, MG Bellemare, A Oord, R Munos International conference on machine learning, 2721-2730, 2017	751	2017
Safe and efficient off-policy reinforcement learning R Munos, T Stepleton, A Harutyunyan, M Bellemare Advances in neural information processing systems 29, 2016	738	2016
Successor features for transfer in reinforcement learning A Barreto, W Dabney, R Munos, JJ Hunt, T Schaul, HP van Hasselt, ... Advances in neural information processing systems 30, 2017	672	2017
Finite-Time Bounds for Fitted Value Iteration. R Munos, C Szepesvári Journal of Machine Learning Research 9 (5), 2008	659	2008
Automated curriculum learning for neural networks A Graves, MG Bellemare, J Menick, R Munos, K Kavukcuoglu international conference on machine learning, 1311-1320, 2017	642	2017
Implicit quantile networks for distributional reinforcement learning W Dabney, G Ostrovski, D Silver, R Munos International conference on machine learning, 1096-1105, 2018	634	2018
Pure exploration in multi-armed bandits problems S Bubeck, R Munos, G Stoltz Algorithmic Learning Theory: 20th International Conference, ALT 2009, Porto …, 2009	633	2009
Recurrent experience replay in distributed reinforcement learning S Kapturowski, G Ostrovski, J Quan, R Munos, W Dabney International conference on learning representations, 2018	581	2018
Modiﬁcation of UCT with Patterns in Monte-Carlo Go S Gelly, Y Wang, R Munos, O Teytaud INRIA, 2006	542	2006

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