| 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 | 366 | 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 | 343* | 2018 |
| Black-box -divergence Minimization JM Hernández-Lobato, Y Li, M Rowland, D Hernández-Lobato, T Bui, ... arXiv preprint arXiv:1511.03243, 2015 | 198 | 2015 |
| 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 | 91 | 2018 |
| 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 | 79 | 2018 |
| Revisiting fundamentals of experience replay W Fedus, P Ramachandran, R Agarwal, Y Bengio, H Larochelle, ... International Conference on Machine Learning, 3061-3071, 2020 | 74 | 2020 |
| α-rank: Multi-agent evaluation by evolution S Omidshafiei, C Papadimitriou, G Piliouras, K Tuyls, M Rowland, ... Scientific reports 9 (1), 1-29, 2019 | 68 | 2019 |
| The unreasonable effectiveness of structured random orthogonal embeddings KM Choromanski, M Rowland, A Weller Advances in neural information processing systems 30, 2017 | 60 | 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 | 46 | 2019 |
| Meta-learning of sequential strategies PA Ortega, JX Wang, M Rowland, T Genewein, Z Kurth-Nelson, ... arXiv preprint arXiv:1905.03030, 2019 | 45 | 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 | 44 | 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 | 31 | 2021 |
| Magnetic hamiltonian monte carlo N Tripuraneni, M Rowland, Z Ghahramani, R Turner International Conference on Machine Learning, 3453-3461, 2017 | 31 | 2017 |
| Geometrically coupled monte carlo sampling M Rowland, KM Choromanski, F Chalus, A Pacchiano, T Sarlos, ... Advances in Neural Information Processing Systems 31, 2018 | 25 | 2018 |
| 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 | 24 | 2018 |
| Tightness of LP relaxations for almost balanced models A Weller, M Rowland, D Sontag Artificial Intelligence and Statistics, 47-55, 2016 | 24 | 2016 |
| 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 | 23 | 2019 |
| Orthogonal estimation of wasserstein distances M Rowland, J Hron, Y Tang, K Choromanski, T Sarlós, A Weller The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 22 | 2019 |
| Unifying orthogonal monte carlo methods K Choromanski, M Rowland, W Chen, A Weller International Conference on Machine Learning, 1203-1212, 2019 | 21 | 2019 |
| 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 | 20 | 2021 |
Rémi MunosDeepMindVerified email at inria.fr
