| Monotonic value function factorisation for deep multi-agent reinforcement learning T Rashid, M Samvelyan, C Schroeder de Witt, G Farquhar, JN Foerster, ... Journal of Machine Learning Research 21, 2020 | 1813 | 2020 |
| The Starcraft Multi-Agent Challenge M Samvelyan, T Rashid, C Schroeder de Witt, G Farquhar, N Nardelli, ... AAMAS 2019, 2019 | 755 | 2019 |
| Is independent learning all you need in the starcraft multi-agent challenge? CS de Witt, T Gupta, D Makoviichuk, V Makoviychuk, PHS Torr, M Sun, ... arXiv preprint arXiv:2011.09533, 2020 | 157 | 2020 |
| FACMAC: Factored Multi-Agent Centralised Policy Gradients B Peng, T Rashid, C Schroeder de Witt, PA Kamienny, P Torr, W Böhmer, ... Advances in Neural Information Processing Systems 34, 2021 | 131 | 2021 |
| Multi-Agent Common Knowledge Reinforcement Learning C Schroeder de Witt, J Foerster, G Farquhar, P Torr, W Boehmer, ... Advances in Neural Information Processing Systems, 9927-9939, 2019 | 102* | 2019 |
| Randomized entity-wise factorization for multi-agent reinforcement learning S Iqbal, CAS De Witt, B Peng, W Böhmer, S Whiteson, F Sha International Conference on Machine Learning, 4596-4606, 2021 | 64* | 2021 |
| Deep Multi-Agent Reinforcement Learning for Decentralized Continuous Cooperative Control C Schroeder de Witt, B Peng, PA Kamienny, P Torr, W Böhmer, ... arXiv preprint arXiv:2003.06709, 2020 | 63 | 2020 |
| The ZX-Calculus is Incomplete for Quantum Mechanics C Schroeder de Witt, V Zamdzhiev Quantum Physics and Logic (QPL) 2014, 2014 | 42* | 2014 |
| Model-free opponent shaping C Lu, T Willi, CAS De Witt, J Foerster International Conference on Machine Learning, 14398-14411, 2022 | 26 | 2022 |
| Safe Screening for Support Vector Machines J Zimmert, C Schroeder de Witt, G Kerg, M Kloft "Optimization in Machine Learning (OPT)" Workshop @ NIPS 2015, 2015 | 22 | 2015 |
| Discovered policy optimisation C Lu, J Kuba, A Letcher, L Metz, C Schroeder de Witt, J Foerster Advances in Neural Information Processing Systems 35, 16455-16468, 2022 | 19 | 2022 |
| Rainbench: Towards data-driven global precipitation forecasting from satellite imagery CS de Witt, C Tong, V Zantedeschi, D De Martini, A Kalaitzis, M Chantry, ... Proceedings of the AAAI Conference on Artificial Intelligence 35 (17), 14902 …, 2021 | 16* | 2021 |
| Hijacking Malaria Simulators with Probabilistic Programming B Gram-Hansen, C Schröder de Witt, T Rainforth, PHS Torr, YW Teh, ... "AI for Social Good Workshop" @ ICML 2019, 2019 | 12* | 2019 |
| Is independent learning all you need in the starcraft multi-agent challenge? C Schroeder de Witt, T Gupta, D Makoviichuk, V Makoviychuk, PHS Torr, ... arXiv e-prints, arXiv: 2011.09533, 2020 | 11 | 2020 |
| Equivariant networks for zero-shot coordination D Muglich, C Schroeder de Witt, E van der Pol, S Whiteson, J Foerster Advances in Neural Information Processing Systems 35, 6410-6423, 2022 | 10 | 2022 |
| Mirror learning: A unifying framework of policy optimisation J Grudzien, CAS De Witt, J Foerster International Conference on Machine Learning, 7825-7844, 2022 | 10* | 2022 |
| Artificial Intelligence & Climate Change: Supplementary Impact Report T Walsh, A Evatt, C Schroeder de Witt | 10 | 2020 |
| Amortized Rejection Sampling in Universal Probabilistic Programming FW Saeid Naderiparizi, Adam Ścibior, Andreas Munk, Mehrdad Ghadiri, Atılım ... AISTATS 2022, 2022 | 9* | 2022 |
| Revealing robust oil and gas company macro-strategies using deep multi-agent reinforcement learning D Radovic, L Kruitwagen, CS de Witt, B Caldecott, S Tomlinson, ... arXiv preprint arXiv:2211.11043, 2022 | 7* | 2022 |
| Towards data-driven physics-informed global precipitation forecasting from satellite imagery V Zantedeschi, D De Martini, C Tong, CS de Witt, A Kalaitzis, M Chantry, ... Proceedings of the AI for Earth Sciences Workshop at NeurIPS, 2020 | 6 | 2020 |
Shimon WhitesonProfessor of Computer Science, University of Oxford / Senior Staff Research Scientist, WaymoVerified email at cs.ox.ac.uk
