| 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 | 2801 | 2020 |
| The Starcraft Multi-Agent Challenge M Samvelyan, T Rashid, C Schroeder de Witt, G Farquhar, N Nardelli, ... AAMAS 2019, 2019 | 1190 | 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 | 385 | 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 | 256 | 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 | 124* | 2019 |
| Foundational challenges in assuring alignment and safety of large language models U Anwar, A Saparov, J Rando, D Paleka, M Turpin, P Hase, ES Lubana, ... arXiv preprint arXiv:2404.09932, 2024 | 116 | 2024 |
| 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 | 97 | 2020 |
| 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 | 90* | 2021 |
| 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 | 82 | 2022 |
| Model-free opponent shaping C Lu, T Willi, CAS De Witt, J Foerster International Conference on Machine Learning, 14398-14411, 2022 | 56 | 2022 |
| The ZX-Calculus is Incomplete for Quantum Mechanics C Schroeder de Witt, V Zamdzhiev Quantum Physics and Logic (QPL) 2014, 2014 | 48* | 2014 |
| JaxMARL: Multi-Agent RL Environments and Algorithms in JAX A Rutherford, B Ellis, M Gallici, J Cook, A Lupu, G Ingvarsson, T Willi, ... Proceedings of the 23rd International Conference on Autonomous Agents and …, 2024 | 46* | 2024 |
| Mirror learning: A unifying framework of policy optimisation J Grudzien, CAS De Witt, J Foerster International Conference on Machine Learning, 7825-7844, 2022 | 29* | 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 | 29* | 2021 |
| 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 | 26* | 2023 |
| 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 | 25 | 2015 |
| 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 | 19 | 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 | 16* | 2019 |
| Risks and opportunities of open-source generative AI F Eiras, A Petrov, B Vidgen, C Schroeder, F Pizzati, K Elkins, ... arXiv preprint arXiv:2405.08597, 2024 | 14 | 2024 |
| Near to mid-term risks and opportunities of open source generative ai F Eiras, A Petrov, B Vidgen, CS de Witt, F Pizzati, K Elkins, ... arXiv preprint arXiv:2404.17047, 2024 | 13 | 2024 |
Jakob FoersterAssociate Professor, University of Oxford在 eng.ox.ac.uk 的电子邮件经过验证