| 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 | 1300 | 2020 |
| The Starcraft Multi-Agent Challenge M Samvelyan, T Rashid, C Schroeder de Witt, G Farquhar, N Nardelli, ... AAMAS 2019, 2019 | 529 | 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 | 94 | 2020 |
| 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 | 89* | 2019 |
| 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 | 63 | 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 | 46 | 2020 |
| The ZX-Calculus is Incomplete for Quantum Mechanics C Schroeder de Witt, V Zamdzhiev Quantum Physics and Logic (QPL) 2014, 2014 | 42* | 2014 |
| 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 | 37* | 2021 |
| 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 | 20 | 2015 |
| Model-free opponent shaping C Lu, T Willi, CAS De Witt, J Foerster International Conference on Machine Learning, 14398-14411, 2022 | 11 | 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 | 10* | 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 | 9 | 2019 |
| Artificial Intelligence & Climate Change: Supplementary Impact Report T Walsh, A Evatt, C Schroeder de Witt | 7 | 2020 |
| Mirror learning: A unifying framework of policy optimisation J Grudzien, CAS De Witt, J Foerster International Conference on Machine Learning, 7825-7844, 2022 | 6* | 2022 |
| 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 | 5 | 2022 |
| Amortized Rejection Sampling in Universal Probabilistic Programming FW Saeid Naderiparizi, Adam Ścibior, Andreas Munk, Mehrdad Ghadiri, Atılım ... AISTATS 2022, 2022 | 5* | 2022 |
| Stratospheric Aerosol Injection as a Deep Reinforcement Learning Problem C Schroeder de Witt, T Hornigold "Tackling Climate Change with Machine Learning" Workshop @ ICML 2019 …, 2019 | 5* | 2019 |
| 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 | 4* | 2022 |
| Communicating via Markov Decision Processes S Sokota, CAS De Witt, M Igl, LM Zintgraf, P Torr, M Strohmeier, Z Kolter, ... International Conference on Machine Learning, 20314-20328, 2022 | 4 | 2022 |
| Simulation-Based Inference for Global Health Decisions C Schroeder de Witt, B Gram-Hansen, N Nardelli, A Gambardella, ... ML for Global Health Workshop at ICML 2020, 2020 | 4* | 2020 |
Jakob FoersterAssociate Professor, University of OxfordVerified email at eng.ox.ac.uk
