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Christian Schroeder de Witt
Christian Schroeder de Witt
Verified email at robots.ox.ac.uk - Homepage
Title
Cited by
Cited by
Year
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
26712020
The Starcraft Multi-Agent Challenge
M Samvelyan, T Rashid, C Schroeder de Witt, G Farquhar, N Nardelli, ...
AAMAS 2019, 2019
11422019
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
3632020
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
2372021
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
122*2019
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
932020
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
852024
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
85*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
772022
Model-free opponent shaping
C Lu, T Willi, CAS De Witt, J Foerster
International Conference on Machine Learning, 14398-14411, 2022
562022
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, ...
The Thirty-eight Conference on Neural Information Processing Systems …, 2024
36*2024
Mirror learning: A unifying framework of policy optimisation
J Grudzien, CAS De Witt, J Foerster
International Conference on Machine Learning, 7825-7844, 2022
26*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
26*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
242015
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
23*2023
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
162022
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
15*2019
Amortized Rejection Sampling in Universal Probabilistic Programming
FW Saeid Naderiparizi, Adam Ścibior, Andreas Munk, Mehrdad Ghadiri, Atılım ...
AISTATS 2022, 2022
10*2022
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
92024
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Articles 1–20