Interpretable Learning Professors in Global

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Yuhong Guo
Carleton University Canada
representation learning
self-supervised learning
continual learning
federated learning
unsupervised learning
reinforcement learning
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Yuhong Guo
Carleton University Canada
representation learning
self-supervised learning
continual learning
federated learning
unsupervised learning
reinforcement learning
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C. Green
University of Wisconsin–Madison United States of America
perceptual learning
human learning
cognitive learning
transfer learning
motor learning
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Fei Wang
Cornell University Netherlands
model interpretability
transfer learning
explainability
federated learning
multi-modal learning
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Fei Wang
Cornell University Netherlands
model interpretability
transfer learning
explainability
federated learning
multi-modal learning
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Pascal Poupart
University of Waterloo Canada
causal learning
self-supervised learning
continual learning
federated learning
reinforcement learning
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Benedek Kurdi
University of Illinois United States of America
learning
causal learning
propositional learning
reinforcement learning
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Jessica Lee
University of Sydney Australia
associative learning
causal learning
rule learning
inductive reasoning
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Pieter Abbeel
University of California, Berkeley United States of America
imitation learning
transfer learning
unsupervised learning
reinforcement learning
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Sascha Schneider
University of Zurich Switzerland
cognitive learning
motivational learning
social learning
emotional learning
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Randall Balestriero
Brown University United States of America
learnable signal processing
representation learning
self-supervised learning
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Shai Ben-david
University of Waterloo Canada
interpretability in machine learning
transfer learning
unsupervised learning
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Moira Maley
University of Western Australia Australia
transformative learning
experiential learning
video learning
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Ehsan Mobaraki
Aalborg University Denmark
explainable artificial intelligence
knowledge representation learning
explainability
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Natalie Bennett
Monash University Australia
collaborative learning
blended learning
constructivist learning
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Yaoliang Yu
University of Waterloo Canada
representation learning
distributed learning
federated learning
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Yue Tan
University of New South Wales Australia
representation learning
multimodal learning
federated learning
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Raymond Mooney
University of Texas at Austin United States of America
explainable ai
transfer learning
ensemble learning
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Rudolf Lioutikov
University of Texas at Austin United States of America
imitation learning
structure learning
reinforcement learning
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Nicholas Rhinehart
University of Toronto Canada
imitation learning
robot learning
reinforcement learning
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Yinbei Li
Singapore Institute of Technology Singapore
imitation learning
robot learning
reinforcement learning
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