Multi Task Learning Professors in Global

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Yuhong Guo
Carleton University Canada
self-supervised learning
reinforcement learning
semi-supervised learning
active learning
machine learning
representation learning
continual learning
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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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Giorgia Ramponi
University of Zurich Switzerland
multi-agent learning
imitation learning
reinforcement learning
machine learning
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Linus Ericsson
University of Glasgow United Kingdom
multi-modal learning
automated machine learning
machine learning
representation learning
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Alexander Rakhlin
Massachusetts Institute of Technology United States of America
reinforcement learning
statistical learning
online learning
machine learning
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Christian Stöhr
Chalmers University of Technology Sweden
digital learning
online learning
hybrid learning
technology-enhanced learning
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Yichen Zhang
Purdue University United States of America
multi-task learning
reinforcement learning
distributed learning
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Abhishek Gupta
University of Washington United States of America
multi-task learning
machine learning
deep reinforcement learning
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Yinbei Li
Singapore Institute of Technology Singapore
robot learning
imitation learning
reinforcement learning
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Wendelin Böhmer
Delft University of Technology Netherlands
multi-agent reinforcement learning
reinforcement learning
machine learning
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Sindri Magnússon
Stockholm University Sweden
multi-agent learning
reinforcement learning
machine learning
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Matthew Howard
King's College London United Kingdom
imitation learning
reinforcement learning
machine learning
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Sharon Flecknoe
Monash University Australia
blended learning
collaborative learning
active learning
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Qingyun Wu
Pennsylvania State University United States of America
learning
reinforcement learning
automated machine learning
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Rudolf Lioutikov
University of Texas at Austin United States of America
imitation learning
reinforcement learning
structure learning
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Lia Fluit
Radboud University Netherlands
complex skills learning
workplace learning
interprofessional learning
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Oya Celiktutan
King's College London United Kingdom
multimodal machine learning
reinforcement learning
continual learning
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Florian Shkurti
University of Toronto Canada
imitation learning
reinforcement learning
machine learning
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Natasha Jaques
University of Washington United States of America
multi-agent learning
social reinforcement learning
machine learning
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Joseph Williams
University of Toronto Canada
learning
reinforcement learning
machine learning
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Florian Shkurti
University of Toronto Canada
imitation learning
reinforcement learning
machine learning
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