Continuous Time Reinforcement Learning Professors in Global

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Yuhong Guo
Carleton University Canada
reinforcement learning
continual learning
self-supervised learning
representation learning
semi-supervised learning
machine learning
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Mehrdad Mahdavi
Pennsylvania State University United States of America
computational learning theory
statistical learning theory
computational statistical machine learning
deep learning
large-scale machine learning
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Kevin Luck
Vrije Universiteit Amsterdam Netherlands
reinforcement learning
deep reinforcement learning
robot learning
deep learning
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Florian Shkurti
University of Toronto Canada
reinforcement learning
inverse reinforcement learning
imitation learning
machine learning
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Jason Lee
University of California, Berkeley United States of America
reinforcement learning
representation learning
deep learning
machine learning
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Jason Lee
University of California, Berkeley United States of America
reinforcement learning
representation learning
deep learning
machine learning
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Irina Rish
University of Montreal Canada
continual lifelong learning
biologically plausible reinforcement learning
deep learning
machine learning
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Maryam Fazel
University of Washington United States of America
reinforcement learning
deep learning
machine learning
learning control
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C. Green
University of Wisconsin–Madison United States of America
human learning
motor learning
cognitive learning
perceptual learning
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Honglak Lee
University of Michigan United States of America
large-scale learning
representation learning
deep learning
machine learning
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Rupam Mahmood
University of Alberta Canada
reinforcement learning
continual learning
real-time learning systems
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Muhammad Hafez
University of Southampton United Kingdom
continual robot learning
reinforcement learning
robot learning
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Colin Bellinger
University of Ottawa Canada
reinforcement learning
safe reinforcement learning
machine learning
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Adam White
University of Alberta Canada
reinforcement learning
continual learning
machine learning
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Katya Scheinberg
Georgia Institute of Technology United States of America
reinforcement learning
continuous optimization
machine learning
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Yinyu Ye
Stanford University United States of America
reinforcement learning
continuous optimization
machine learning
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Alexander Rakhlin
Massachusetts Institute of Technology United States of America
reinforcement learning
statistical learning
machine learning
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Aditya Mahajan
McGill University Canada
reinforcement learning
multi-agent reinforcement learning
stochastic iterative algorithms
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Adam Sobey
University of Southampton United Kingdom
reinforcement learning
continuous environments
machine learning
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Wendelin Böhmer
Delft University of Technology Netherlands
reinforcement learning
multi-agent reinforcement learning
machine learning
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Yinbei Li
Singapore Institute of Technology Singapore
reinforcement learning
robot learning
imitation learning
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