| Fast context adaptation via meta-learning L Zintgraf, K Shiarli, V Kurin, K Hofmann, S Whiteson International Conference on Machine Learning, 7693-7702, 2019 | 339 | 2019 |
| Deep coordination graphs W Böhmer, V Kurin, S Whiteson International Conference on Machine Learning, 980-991, 2020 | 130 | 2020 |
| Learning from demonstration in the wild F Behbahani, K Shiarlis, X Chen, V Kurin, S Kasewa, C Stirbu, J Gomes, ... 2019 International Conference on Robotics and Automation (ICRA), 775-781, 2019 | 59 | 2019 |
| The atari grand challenge dataset V Kurin, S Nowozin, K Hofmann, L Beyer, B Leibe arXiv preprint arXiv:1705.10998, 2017 | 48 | 2017 |
| Minihack the planet: A sandbox for open-ended reinforcement learning research M Samvelyan, R Kirk, V Kurin, J Parker-Holder, M Jiang, E Hambro, ... arXiv preprint arXiv:2109.13202, 2021 | 43 | 2021 |
| My body is a cage: the role of morphology in graph-based incompatible control V Kurin, M Igl, T Rocktäschel, W Boehmer, S Whiteson arXiv preprint arXiv:2010.01856, 2020 | 34 | 2020 |
| Fast efficient hyperparameter tuning for policy gradient methods S Paul, V Kurin, S Whiteson Advances in Neural Information Processing Systems 32, 2019 | 33 | 2019 |
| Towards a principled integration of multi-camera re-identification and tracking through optimal bayes filters L Beyer, S Breuers, V Kurin, B Leibe Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017 | 30 | 2017 |
| Can Q-learning with graph networks learn a generalizable branching heuristic for a SAT solver? V Kurin, S Godil, S Whiteson, B Catanzaro Advances in Neural Information Processing Systems 33, 9608-9621, 2020 | 29 | 2020 |
| Improving SAT solver heuristics with graph networks and reinforcement learning V Kurin, S Godil, S Whiteson, B Catanzaro | 27 | 2019 |
| Fast efficient hyperparameter tuning for policy gradients S Paul, V Kurin, S Whiteson arXiv preprint arXiv:1902.06583, 2019 | 27 | 2019 |
| In defense of the unitary scalarization for deep multi-task learning V Kurin, A De Palma, I Kostrikov, S Whiteson, PK Mudigonda Advances in Neural Information Processing Systems 35, 12169-12183, 2022 | 24 | 2022 |
| A generalist neural algorithmic learner B Ibarz, V Kurin, G Papamakarios, K Nikiforou, M Bennani, R Csordás, ... Learning on Graphs Conference, 2: 1-2: 23, 2022 | 9 | 2022 |
| You May Not Need Ratio Clipping in PPO M Sun, V Kurin, G Liu, S Devlin, T Qin, K Hofmann, S Whiteson arXiv preprint arXiv:2202.00079, 2022 | 8 | 2022 |
| Snowflake: Scaling GNNs to high-dimensional continuous control via parameter freezing C Blake, V Kurin, M Igl, S Whiteson Advances in Neural Information Processing Systems 34, 23983-23992, 2021 | 8 | 2021 |
| Insights from the neurips 2021 nethack challenge E Hambro, S Mohanty, D Babaev, M Byeon, D Chakraborty, ... NeurIPS 2021 Competitions and Demonstrations Track, 41-52, 2022 | 7 | 2022 |
| A minimalist approach to deep multi-task learning V Kurin University of Oxford, 2022 | | 2022 |
| Behaviour Models for Autonomous Vehicle Simulators SA Whiteson, J Messias, X Chen, F Behbahani, K Shiarli, S Kasewa, ... US Patent App. 16/978,446, 2021 | | 2021 |
| AIMS CDT Project Report: Towards One-Shot Learning From Demonstration via Reinforcement Learning MN Finean, LM Zintgraf, K Shiarlis, V Kurin, S Whiteson | | |
Shimon WhitesonProfessor of Computer Science, University of Oxford / Head of Research, Waymo UKVerified email at cs.ox.ac.uk
