Mark Rowland

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Citations	3236	3165
h-index	26	26
i10-index	35	35

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Rémi MunosDeepMindVerified email at inria.fr
Will DabneyDeepMindVerified email at google.com
Marc G. BellemareGoogle BrainVerified email at google.com
Karl TuylsResearch Scientist, Google DeepMind and Professor of computer science, University of LiverpoolVerified email at google.com
Adrian WellerDirector of Research, Machine Learning, University of CambridgeVerified email at eng.cam.ac.uk
julien perolatDeepMindVerified email at google.com
Richard E TurnerProfessor, University of CambridgeVerified email at cam.ac.uk
Shayegan OmidshafieiGoogle Research (previously: DeepMind, MIT)Verified email at google.com
Jiri HronResearch Scientist, Google DeepMindVerified email at google.com
Alexander G. D. G. MatthewsDeepMindVerified email at google.com
Krzysztof ChoromanskiGoogle Brain Robotics New York & Columbia UniversityVerified email at columbia.edu
Yunhao TangResearch Scientist, DeepMindVerified email at columbia.edu
Michal ValkoGoogle DeepMind Paris & Inria France & ENS MVAVerified email at deepmind.com
Georgios PiliourasGoogle DeepMind, Singapore University of Technology and DesignVerified email at google.com
Zoubin GhahramaniProfessor, University of Cambridge, and Distinguished Researcher, GoogleVerified email at eng.cam.ac.uk
Marc LanctotResearch Scientist, DeepMindVerified email at google.com
Daniel HennesResearch Scientist, Google DeepMindVerified email at google.com
Paul MullerResearch Scientist, DeepmindVerified email at google.com
Mohammad Gheshlaghi AzarResearch Scientist at DeepMindVerified email at google.com
Yee Whye TehProfessor of Statistical Machine Learning, Oxford, Research Scientist, DeepMindVerified email at stats.ox.ac.uk

Mark Rowland

Research Scientist, Google DeepMind

Verified email at google.com - Homepage

Reinforcement Learning Statistics Probability


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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	671	2018
Gaussian process behaviour in wide deep neural networks AGG Matthews, M Rowland, J Hron, RE Turner, Z Ghahramani arXiv preprint arXiv:1804.11271, 2018	483*	2018
Black-box -divergence Minimization JM Hernández-Lobato, Y Li, M Rowland, D Hernández-Lobato, T Bui, ... arXiv preprint arXiv:1511.03243, 2015	249	2015
Revisiting fundamentals of experience replay W Fedus, P Ramachandran, R Agarwal, Y Bengio, H Larochelle, ... International Conference on Machine Learning, 3061-3071, 2020	217	2020
Structured evolution with compact architectures for scalable policy optimization K Choromanski, M Rowland, V Sindhwani, R Turner, A Weller International Conference on Machine Learning, 970-978, 2018	135	2018
α-Rank: Multi-Agent Evaluation by Evolution S Omidshafiei, C Papadimitriou, G Piliouras, K Tuyls, M Rowland, ... Scientific reports 9 (1), 9937, 2019	111	2019
An analysis of categorical distributional reinforcement learning M Rowland, M Bellemare, W Dabney, R Munos, YW Teh International Conference on Artificial Intelligence and Statistics, 29-37, 2018	111	2018
A generalized training approach for multiagent learning P Muller, S Omidshafiei, M Rowland, K Tuyls, J Perolat, S Liu, D Hennes, ... arXiv preprint arXiv:1909.12823, 2019	88	2019
Mastering the game of Stratego with model-free multiagent reinforcement learning J Perolat, B De Vylder, D Hennes, E Tarassov, F Strub, V de Boer, ... Science 378 (6623), 990-996, 2022	85	2022
The unreasonable effectiveness of structured random orthogonal embeddings KM Choromanski, M Rowland, A Weller Advances in neural information processing systems 30, 2017	83	2017
Statistics and samples in distributional reinforcement learning M Rowland, R Dadashi, S Kumar, R Munos, MG Bellemare, W Dabney International Conference on Machine Learning, 5528-5536, 2019	80	2019
Distributional reinforcement learning MG Bellemare, W Dabney, M Rowland MIT Press, 2023	74	2023
Meta-learning of sequential strategies PA Ortega, JX Wang, M Rowland, T Genewein, Z Kurth-Nelson, ... arXiv preprint arXiv:1905.03030, 2019	73	2019
Game Plan: What AI can do for Football, and What Football can do for AI K Tuyls, S Omidshafiei, P Muller, Z Wang, J Connor, D Hennes, I Graham, ... Journal of Artificial Intelligence Research 71, 41-88, 2021	72	2021
From Poincaré recurrence to convergence in imperfect information games: Finding equilibrium via regularization J Perolat, R Munos, JB Lespiau, S Omidshafiei, M Rowland, P Ortega, ... International Conference on Machine Learning, 8525-8535, 2021	63	2021
The value-improvement path: Towards better representations for reinforcement learning W Dabney, A Barreto, M Rowland, R Dadashi, J Quan, MG Bellemare, ... Proceedings of the AAAI Conference on Artificial Intelligence 35 (8), 7160-7168, 2021	57	2021
On the effect of auxiliary tasks on representation dynamics C Lyle, M Rowland, G Ostrovski, W Dabney International Conference on Artificial Intelligence and Statistics, 1-9, 2021	55	2021
Understanding and preventing capacity loss in reinforcement learning C Lyle, M Rowland, W Dabney arXiv preprint arXiv:2204.09560, 2022	42	2022
Orthogonal estimation of wasserstein distances M Rowland, J Hron, Y Tang, K Choromanski, T Sarlos, A Weller The 22nd International Conference on Artificial Intelligence and Statistics …, 2019	42	2019
Magnetic hamiltonian monte carlo N Tripuraneni, M Rowland, Z Ghahramani, R Turner International Conference on Machine Learning, 3453-3461, 2017	42	2017

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