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Christian Schroeder de Witt
Christian Schroeder de Witt
Bestätigte E-Mail-Adresse bei robots.ox.ac.uk - Startseite
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Zitiert von
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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
11892020
The Starcraft Multi-Agent Challenge
M Samvelyan, T Rashid, C Schroeder de Witt, G Farquhar, N Nardelli, ...
AAMAS 2019, 2019
4752019
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
83*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
802020
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
512021
The ZX-Calculus is Incomplete for Quantum Mechanics
C Schroeder de Witt, V Zamdzhiev
Quantum Physics and Logic (QPL) 2014, 2014
41*2014
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
402020
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
34*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
202015
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
10*2021
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
92019
Artificial Intelligence & Climate Change: Supplementary Impact Report
T Walsh, A Evatt, C Schroeder de Witt
72020
Model-free opponent shaping
C Lu, T Willi, CAS De Witt, J Foerster
International Conference on Machine Learning, 14398-14411, 2022
62022
Mirror learning: A unifying framework of policy optimisation
J Grudzien, CAS De Witt, J Foerster
International Conference on Machine Learning, 7825-7844, 2022
5*2022
Amortized Rejection Sampling in Universal Probabilistic Programming
FW Saeid Naderiparizi, Adam Ścibior, Andreas Munk, Mehrdad Ghadiri, Atılım ...
AISTATS 2022, 2022
5*2022
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
5*2019
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
4*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
42020
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
3*2022
Discovered policy optimisation
C Lu, JG Kuba, A Letcher, L Metz, CS de Witt, J Foerster
arXiv preprint arXiv:2210.05639, 2022
32022
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