Christian Schroeder de Witt
Christian Schroeder de Witt
Vahvistettu sähköpostiosoite verkkotunnuksessa robots.ox.ac.uk
Nimike
Viittaukset
Viittaukset
Vuosi
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
2792018
The Starcraft Multi-Agent Challenge
M Samvelyan, T Rashid, C Schroeder de Witt, G Farquhar, N Nardelli, ...
AAMAS 2019, 2019
782019
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
35*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
312019
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
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
72020
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
52020
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
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
2*2019
Artificial Intelligence & Climate Change: Supplementary Impact Report
T Walsh, A Evatt, C Schroeder de Witt
12020
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
12019
Efficient Bayesian inference for nested simulators
B Gram-Hansen, C Schroeder de Witt, R Zinkov, S Naderiparizi, A Scibior, ...
12019
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
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
2020
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
2020
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
2020
Revealing the Oil Majors’ Adaptive Capacity to the Energy Transition with Deep Multi-Agent Reinforcement Learning
D Radovic, L Kruitwagen, C Schroeder de Witt
"Tackling Climate Change with Machine Learning" Workshop @ NeurIPS 2020, 2020
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
2020
RainBench: Enabling Data-Driven Precipitation Forecasting on a Global Scale
C Tong, ...
"Tackling Climate Change with Machine Learning" Workshop @ NeurIPS 2020, 2020
2020
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Artikkelit 1–19