Christian Schroeder de Witt
Christian Schroeder de Witt
Verified email at robots.ox.ac.uk
Title
Cited by
Cited by
Year
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
4362018
The Starcraft Multi-Agent Challenge
M Samvelyan, T Rashid, C Schroeder de Witt, G Farquhar, N Nardelli, ...
AAMAS 2019, 2019
1492019
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
36*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
272019
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
252020
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
18*2020
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
142015
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
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
32019
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
22020
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
22019
Artificial Intelligence & Climate Change: Supplementary Impact Report
T Walsh, A Evatt, C Schroeder de Witt
12020
Efficient Bayesian inference for nested simulators
B Gram-Hansen, C Schroeder de Witt, R Zinkov, S Naderiparizi, A Scibior, ...
12019
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
1*2019
Implicit Communication as Minimum Entropy Coupling
S Sokota, CS de Witt, M Igl, L Zintgraf, P Torr, S Whiteson, J Foerster
arXiv preprint arXiv:2107.08295, 2021
2021
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
2021
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
2021
A Self-Supervised Auxiliary Loss for Deep RL in Partially Observable Settings
E Ahmed, L Zintgraf, CAS de Witt, N Usunier
arXiv preprint arXiv:2104.08492, 2021
2021
RainBench: Enabling Data-Driven Precipitation Forecasting on a Global Scale
C Schroeder de Witt, C Tong, V Zantedeschi, D De Martini, A Kalaitzis, ...
EGU General Assembly Conference Abstracts, EGU21-1762, 2021
2021
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
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