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 | 2115 | 2020 |
The Starcraft Multi-Agent Challenge M Samvelyan, T Rashid, C Schroeder de Witt, G Farquhar, N Nardelli, ... AAMAS 2019, 2019 | 912 | 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 | 236 | 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 | 168 | 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 | 111* | 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 | 73* | 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 | 72 | 2020 |
The ZX-Calculus is Incomplete for Quantum Mechanics C Schroeder de Witt, V Zamdzhiev Quantum Physics and Logic (QPL) 2014, 2014 | 44* | 2014 |
Model-free opponent shaping C Lu, T Willi, CAS De Witt, J Foerster International Conference on Machine Learning, 14398-14411, 2022 | 38 | 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 | 35 | 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 |
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 | 21* | 2021 |
Mirror learning: A unifying framework of policy optimisation J Grudzien, CAS De Witt, J Foerster International Conference on Machine Learning, 7825-7844, 2022 | 19* | 2022 |
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 | 15 | 2022 |
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 | 14* | 2019 |
Perfectly Secure Steganography Using Minimum Entropy Coupling C Schroeder de Witt*, S Sokota*, JZ Kolter, J Foerster, M Strohmeier ICLR 2023 (featured by Scientific American, Quanta Magazine, Bruce Schneier …, 2023 | 12* | 2023 |
Artificial Intelligence & Climate Change: Supplementary Impact Report T Walsh, A Evatt, C Schroeder de Witt | 12 | 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 | 11 | 2020 |
Amortized Rejection Sampling in Universal Probabilistic Programming FW Saeid Naderiparizi, Adam Ścibior, Andreas Munk, Mehrdad Ghadiri, Atılım ... AISTATS 2022, 2022 | 8* | 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 |