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Mark Rowland
Mark Rowland
Research Scientist, Google DeepMind
Verified email at google.com - Homepage
Title
Cited by
Cited by
Year
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
7352018
Gaussian process behaviour in wide deep neural networks
AGG Matthews, M Rowland, J Hron, RE Turner, Z Ghahramani
arXiv preprint arXiv:1804.11271, 2018
514*2018
Black-box -divergence Minimization
JM Hernández-Lobato, Y Li, M Rowland, D Hernández-Lobato, T Bui, ...
arXiv preprint arXiv:1511.03243, 2015
2562015
Revisiting fundamentals of experience replay
W Fedus, P Ramachandran, R Agarwal, Y Bengio, H Larochelle, ...
International Conference on Machine Learning, 3061-3071, 2020
2482020
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
1422018
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
1372022
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
1262018
α-Rank: Multi-Agent Evaluation by Evolution
S Omidshafiei, C Papadimitriou, G Piliouras, K Tuyls, M Rowland, ...
Scientific reports 9 (1), 9937, 2019
1202019
Distributional reinforcement learning
MG Bellemare, W Dabney, M Rowland
MIT Press, 2023
1022023
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
992019
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
912019
The unreasonable effectiveness of structured random orthogonal embeddings
KM Choromanski, M Rowland, A Weller
Advances in neural information processing systems 30, 2017
912017
Meta-learning of sequential strategies
PA Ortega, JX Wang, M Rowland, T Genewein, Z Kurth-Nelson, ...
arXiv preprint arXiv:1905.03030, 2019
812019
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
802021
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
742021
Understanding and preventing capacity loss in reinforcement learning
C Lyle, M Rowland, W Dabney
arXiv preprint arXiv:2204.09560, 2022
622022
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
622021
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
622021
A general theoretical paradigm to understand learning from human preferences
MG Azar, M Rowland, B Piot, D Guo, D Calandriello, M Valko, R Munos
arXiv preprint arXiv:2310.12036, 2023
472023
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
462019
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