| 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 | 540 | 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 | 440* | 2018 |
| Black-box -divergence Minimization JM Hernández-Lobato, Y Li, M Rowland, D Hernández-Lobato, T Bui, ... arXiv preprint arXiv:1511.03243, 2015 | 233 | 2015 |
| Revisiting fundamentals of experience replay W Fedus, P Ramachandran, R Agarwal, Y Bengio, H Larochelle, ... International Conference on Machine Learning, 3061-3071, 2020 | 152 | 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 | 117 | 2018 |
| α-Rank: Multi-Agent Evaluation by Evolution S Omidshafiei, C Papadimitriou, G Piliouras, K Tuyls, M Rowland, ... Scientific reports 9 (1), 9937, 2019 | 106 | 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 | 99 | 2018 |
| The unreasonable effectiveness of structured random orthogonal embeddings KM Choromanski, M Rowland, A Weller Advances in neural information processing systems 30, 2017 | 75 | 2017 |
| 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 | 69 | 2019 |
| 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 | 62 | 2019 |
| Meta-learning of sequential strategies PA Ortega, JX Wang, M Rowland, T Genewein, Z Kurth-Nelson, ... arXiv preprint arXiv:1905.03030, 2019 | 62 | 2019 |
| 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 | 51 | 2021 |
| 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 | 46 | 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 | 43 | 2021 |
| Magnetic hamiltonian monte carlo N Tripuraneni, M Rowland, Z Ghahramani, R Turner International Conference on Machine Learning, 3453-3461, 2017 | 41 | 2017 |
| 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 | 40 | 2021 |
| Distributional reinforcement learning MG Bellemare, W Dabney, M Rowland MIT Press, 2023 | 36 | 2023 |
| 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 | 34 | 2019 |
| Multiagent evaluation under incomplete information M Rowland, S Omidshafiei, K Tuyls, J Perolat, M Valko, G Piliouras, ... Advances in Neural Information Processing Systems 32, 2019 | 34 | 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 | 34 | 2018 |
Rémi MunosDeepMindVerified email at inria.fr
