Theoretical Reinforcement Learning Professors in Global

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Yuhong Guo
Carleton University Canada
reinforcement learning
self-supervised learning
representation learning
machine learning
weakly supervised learning
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C. Green
University of Wisconsin–Madison United States of America
motor learning
human learning
depth learning
perceptual learning
cognitive 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
machine learning
deep learning
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Jason Lee
University of California, Berkeley United States of America
reinforcement learning
representation learning
machine learning
deep learning
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Brokoslaw Laschowski
University of Toronto Canada
reinforcement learning
machine learning
deep learning
theoretical neuroscience
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Mehrdad Mahdavi
Pennsylvania State University United States of America
statistical learning theory
computational learning theory
large-scale machine learning
deep learning
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Honglak Lee
University of Michigan United States of America
large-scale learning
representation learning
machine learning
deep learning
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Wendelin Böhmer
Delft University of Technology Netherlands
reinforcement learning
multi-agent reinforcement learning
machine learning
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Colin Bellinger
University of Ottawa Canada
reinforcement learning
safe reinforcement learning
machine learning
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Samet Oymak
University of Michigan United States of America
reinforcement learning
statistical learning theory
machine learning
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Yi Zhou
Texas A&M University United States of America
reinforcement learning
statistical learning theory
machine learning
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Aditya Mahajan
McGill University Canada
reinforcement learning
multi-agent reinforcement learning
stochastic iterative algorithms
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Semih Cayci
RWTH Aachen University Germany
reinforcement learning
deep learning theory
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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Ambuj Tewari
University of Michigan United States of America
reinforcement learning
theory of machine learning
machine learning
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Yuxin Chen
University of Pennsylvania United States of America
reinforcement learning
machine learning theory
statistical machine learning
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Florian Shkurti
University of Toronto Canada
reinforcement learning
imitation learning
machine learning
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Giorgia Ramponi
University of Zurich Switzerland
reinforcement learning
imitation learning
machine learning
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Yinbei Li
Singapore Institute of Technology Singapore
reinforcement learning
imitation learning
robot learning
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