Mark Rowland
Mark Rowland
Research Scientist, DeepMind
Verified email at - Homepage
Cited by
Cited by
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
Gaussian process behaviour in wide deep neural networks
AGG Matthews, M Rowland, J Hron, RE Turner, Z Ghahramani
arXiv preprint arXiv:1804.11271, 2018
Black-box -divergence Minimization
JM Hernández-Lobato, Y Li, M Rowland, D Hernández-Lobato, T Bui, ...
arXiv preprint arXiv:1511.03243, 2015
Structured evolution with compact architectures for scalable policy optimization
K Choromanski, M Rowland, V Sindhwani, RE Turner, A Weller
arXiv preprint arXiv:1804.02395, 2018
An analysis of categorical distributional reinforcement learning
M Rowland, MG Bellemare, W Dabney, R Munos, YW Teh
arXiv preprint arXiv:1802.08163, 2018
α-rank: Multi-agent evaluation by evolution
S Omidshafiei, C Papadimitriou, G Piliouras, K Tuyls, M Rowland, ...
Scientific reports 9 (1), 1-29, 2019
The unreasonable effectiveness of structured random orthogonal embeddings
KM Choromanski, M Rowland, A Weller
Advances in neural information processing systems, 219-228, 2017
Meta-learning of sequential strategies
PA Ortega, JX Wang, M Rowland, T Genewein, Z Kurth-Nelson, ...
arXiv preprint arXiv:1905.03030, 2019
Tightness of LP relaxations for almost balanced models
A Weller, M Rowland, D Sontag
Artificial Intelligence and Statistics, 47-55, 2016
Magnetic hamiltonian monte carlo
N Tripuraneni, M Rowland, Z Ghahramani, R Turner
International Conference on Machine Learning, 3453-3461, 2017
Geometrically coupled monte carlo sampling
M Rowland, KM Choromanski, F Chalus, A Pacchiano, T Sarlos, ...
Advances in Neural Information Processing Systems, 195-206, 2018
Multiagent evaluation under incomplete information
M Rowland, S Omidshafiei, K Tuyls, J Perolat, M Valko, G Piliouras, ...
Advances in Neural Information Processing Systems, 12291-12303, 2019
Statistics and samples in distributional reinforcement learning
M Rowland, R Dadashi, S Kumar, R Munos, MG Bellemare, W Dabney
arXiv preprint arXiv:1902.08102, 2019
The geometry of random features
K Choromanski, M Rowland, T Sarlós, V Sindhwani, R Turner, A Weller
International Conference on Artificial Intelligence and Statistics, 1-9, 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
From Poincar\'e Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization
J Perolat, R Munos, JB Lespiau, S Omidshafiei, M Rowland, P Ortega, ...
arXiv preprint arXiv:2002.08456, 2020
Unifying orthogonal monte carlo methods
K Choromanski, M Rowland, W Chen, A Weller
International Conference on Machine Learning, 1203-1212, 2019
Orthogonal estimation of wasserstein distances
M Rowland, J Hron, Y Tang, K Choromanski, T Sarlos, A Weller
arXiv preprint arXiv:1903.03784, 2019
Conditions beyond treewidth for tightness of higher-order LP relaxations
M Rowland, A Pacchiano, A Weller
Artificial Intelligence and Statistics, 10-18, 2017
Revisiting fundamentals of experience replay
W Fedus, P Ramachandran, R Agarwal, Y Bengio, H Larochelle, ...
International Conference on Machine Learning, 3061-3071, 2020
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