| 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 | 167 | 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 | 146 | 2018 |
| Black-box -divergence Minimization JM Hernández-Lobato, Y Li, M Rowland, D Hernández-Lobato, T Bui, ... arXiv preprint arXiv:1511.03243, 2015 | 142 | 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 | 60 | 2018 |
| An analysis of categorical distributional reinforcement learning M Rowland, MG Bellemare, W Dabney, R Munos, YW Teh arXiv preprint arXiv:1802.08163, 2018 | 42 | 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 | 39 | 2019 |
| The unreasonable effectiveness of structured random orthogonal embeddings KM Choromanski, M Rowland, A Weller Advances in neural information processing systems, 219-228, 2017 | 30 | 2017 |
| Meta-learning of sequential strategies PA Ortega, JX Wang, M Rowland, T Genewein, Z Kurth-Nelson, ... arXiv preprint arXiv:1905.03030, 2019 | 28 | 2019 |
| Tightness of LP relaxations for almost balanced models A Weller, M Rowland, D Sontag Artificial Intelligence and Statistics, 47-55, 2016 | 23 | 2016 |
| Magnetic hamiltonian monte carlo N Tripuraneni, M Rowland, Z Ghahramani, R Turner International Conference on Machine Learning, 3453-3461, 2017 | 18 | 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 | 17 | 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 | 15 | 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 | 14 | 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 | 14 | 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 | 11 | 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 | 9 | 2020 |
| Unifying orthogonal monte carlo methods K Choromanski, M Rowland, W Chen, A Weller International Conference on Machine Learning, 1203-1212, 2019 | 9 | 2019 |
| Orthogonal estimation of wasserstein distances M Rowland, J Hron, Y Tang, K Choromanski, T Sarlos, A Weller arXiv preprint arXiv:1903.03784, 2019 | 8 | 2019 |
| Conditions beyond treewidth for tightness of higher-order LP relaxations M Rowland, A Pacchiano, A Weller Artificial Intelligence and Statistics, 10-18, 2017 | 7 | 2017 |
| Revisiting fundamentals of experience replay W Fedus, P Ramachandran, R Agarwal, Y Bengio, H Larochelle, ... International Conference on Machine Learning, 3061-3071, 2020 | 6 | 2020 |
Rémi MunosDeepMindVerified email at inria.fr
