| QMIX: Monotonic value function factorisation for deep multi-agent reinforcement learning T Rashid, M Samvelyan, C Schroeder De Witt, G Farquhar, J Foerster, ... ICML 2019, 2018 | 279 | 2018 |
| The Starcraft Multi-Agent Challenge M Samvelyan, T Rashid, C Schroeder de Witt, G Farquhar, N Nardelli, ... AAMAS 2019, 2019 | 78 | 2019 |
| The ZX-calculus is incomplete for quantum mechanics C Schroeder de Witt, V Zamdzhiev The 11th International Workshop on Quantum Physics and Logic (QPL), 2014 (Kyoto), 2014 | 35* | 2014 |
| 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 | 31 | 2019 |
| 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 | 14 | 2015 |
| Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning T Rashid, M Samvelyan, C Schroeder De Witt, G Farquhar, J Foerster, ... Journal of Machine Learning Research (JMLR) 21 (178), 1−51, 2020 | 7 | 2020 |
| 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 | 5 | 2020 |
| 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 | 3 | 2019 |
| 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 | 2* | 2019 |
| Artificial Intelligence & Climate Change: Supplementary Impact Report T Walsh, A Evatt, C Schroeder de Witt | 1 | 2020 |
| Amortized rejection sampling in universal probabilistic programming S Naderiparizi, A Ścibior, A Munk, M Ghadiri, AG Baydin, B Gram-Hansen, ... arXiv preprint arXiv:1910.09056, 2019 | 1 | 2019 |
| Efficient Bayesian inference for nested simulators B Gram-Hansen, C Schroeder de Witt, R Zinkov, S Naderiparizi, A Scibior, ... | 1 | 2019 |
| RainBench: Towards Global Precipitation Forecasting from Satellite Imagery C Schroeder de Witt, C Tong, V Zantedeschi, D De Martini, F Kalaitzis, ... AAAI 2021, arXiv: 2012.09670, 2020 | | 2020 |
| 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 | | 2020 |
| Randomized Entity-Wise Factorization for Deep Multi-Agent Reinforcement Learning S Iqbal, C Schroeder de Witt, B Peng, W Böhmer, S Whiteson, F Sha arXiv preprint arXiv:2006.04222, 2020 | | 2020 |
| 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 | | 2020 |
| Revealing the Oil Majors’ Adaptive Capacity to the Energy Transition with Deep Multi-Agent Reinforcement Learning D Radovic, L Kruitwagen, C Schroeder de Witt "Tackling Climate Change with Machine Learning" Workshop @ NeurIPS 2020, 2020 | | 2020 |
| Towards Data-Driven Physics-Informed Global Precipitation Forecasting from Satellite Imagery V Zantedeschi, D De Martini, C Tong, C Schroeder de Witt, A Kalaitzis, ... "Tackling Climate Change with Machine Learning" Workshop @ NeurIPS 2020, 2020 | | 2020 |
| RainBench: Enabling Data-Driven Precipitation Forecasting on a Global Scale C Tong, ... "Tackling Climate Change with Machine Learning" Workshop @ NeurIPS 2020, 2020 | | 2020 |
Shimon WhitesonProfessor of Computer Science, University of Oxford / Head of Research, Waymo UKVahvistettu sähköpostiosoite verkkotunnuksessa cs.ox.ac.uk
