| Deterministic policy gradient algorithms D Silver, G Lever, N Heess, T Degris, D Wierstra, M Riedmiller International conference on machine learning, 387-395, 2014 | 3952 | 2014 |
| Value-decomposition networks for cooperative multi-agent learning P Sunehag, G Lever, A Gruslys, WM Czarnecki, V Zambaldi, M Jaderberg, ... arXiv preprint arXiv:1706.05296, 2017 | 1085 | 2017 |
| Human-level performance in 3D multiplayer games with population-based reinforcement learning M Jaderberg, WM Czarnecki, I Dunning, L Marris, G Lever, AG Castaneda, ... Science 364 (6443), 859-865, 2019 | 741 | 2019 |
| Conditional mean embeddings as regressors-supplementary S Grünewälder, G Lever, L Baldassarre, S Patterson, A Gretton, M Pontil arXiv preprint arXiv:1205.4656, 2012 | 150 | 2012 |
| Nesterov's accelerated gradient and momentum as approximations to regularised update descent A Botev, G Lever, D Barber 2017 International joint conference on neural networks (IJCNN), 1899-1903, 2017 | 145 | 2017 |
| Emergent coordination through competition S Liu, G Lever, J Merel, S Tunyasuvunakool, N Heess, T Graepel arXiv preprint arXiv:1902.07151, 2019 | 134 | 2019 |
| Modelling transition dynamics in MDPs with RKHS embeddings S Grunewalder, G Lever, L Baldassarre, M Pontil, A Gretton arXiv preprint arXiv:1206.4655, 2012 | 124 | 2012 |
| Tighter PAC-Bayes bounds through distribution-dependent priors G Lever, F Laviolette, J Shawe-Taylor Theoretical Computer Science 473, 4-28, 2013 | 107 | 2013 |
| 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 |
| Predicting the labelling of a graph via minimum p-seminorm interpolation M Herbster, G Lever NIPS Workshop 2010: Networks Across Disciplines: Theory and Applications, 2009 | 60 | 2009 |
| Online prediction on large diameter graphs M Herbster, G Lever, M Pontil Advances in Neural Information Processing Systems 21, 2008 | 60 | 2008 |
| Biases for emergent communication in multi-agent reinforcement learning T Eccles, Y Bachrach, G Lever, A Lazaridou, T Graepel Advances in neural information processing systems 32, 2019 | 57 | 2019 |
| Distribution-Dependent PAC-Bayes Priors. G Lever, F Laviolette, J Shawe-Taylor ALT, 119-133, 2010 | 54 | 2010 |
| From motor control to team play in simulated humanoid football S Liu, G Lever, Z Wang, J Merel, SM Eslami, D Hennes, WM Czarnecki, ... arXiv preprint arXiv:2105.12196, 2021 | 41 | 2021 |
| Modelling policies in mdps in reproducing kernel hilbert space G Lever, R Stafford Artificial intelligence and statistics, 590-598, 2015 | 41 | 2015 |
| Approximate newton methods for policy search in markov decision processes T Furmston, G Lever, D Barber Journal of Machine Learning Research 17, 2016 | 27 | 2016 |
| Compressed conditional mean embeddings for model-based reinforcement learning G Lever, J Shawe-Taylor, R Stafford, C Szepesvári Proceedings of the AAAI Conference on Artificial Intelligence 30 (1), 2016 | 25 | 2016 |
| Reinforcement learning agents acquire flocking and symbiotic behaviour in simulated ecosystems P Sunehag, G Lever, S Liu, J Merel, N Heess, JZ Leibo, E Hughes, ... ALIFE 2019: The 2019 Conference on Artificial Life, 103-110, 2019 | 23 | 2019 |
| From motor control to team play in simulated humanoid football S Liu, G Lever, Z Wang, J Merel, SMA Eslami, D Hennes, WM Czarnecki, ... Science Robotics 7 (69), eabo0235, 2022 | 14 | 2022 |
| Data dependent kernels in nearly-linear time G Lever, T Diethe, J Shawe-Taylor Artificial Intelligence and Statistics, 685-693, 2012 | 13 | 2012 |
