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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Pascal Poupart
University of Waterloo Canada
causal learning
self-supervised learning
continual learning
federated 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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Yue Tan
University of New South Wales Australia
representation learning
multimodal learning
federated learning
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Joana Carvalheiro
University of Glasgow United Kingdom
punishment learning
reward learning
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Il-min Kim
Queen's University Canada
distributed learning
continual learning
federated learning
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Matthew Gardner
Concordia University Canada
learning behavior
predictive information
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Mikhail Glazunov
Delft University of Technology Netherlands
explainability in ai
unsupervised learning
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Marcelo Worsley
Northwestern University United States of America
multimodal learning
collaborative learning environments
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Jennifer Thom
University of Victoria Canada
mathematical understanding
collective learning
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Lyndon Martin
York University Canada
mathematical understanding
collective learning
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Doris Lewalter-manhart
Technical University of Munich Germany
informal learning
formal learning
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Robert Duke
University of Texas at Austin United States of America
skill learning
human learning behavior
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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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Anqi Liu
Johns Hopkins University United States of America
interactive machine learning
distributionally robust learning
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Felix Weilacher
University of California, Berkeley United States of America
measurable combinatorics
computable combinatorics
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Silvia Tolu
Technical University of Denmark Denmark
cerebellar learning
motor learning
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Adam White
University of Alberta Canada
continual learning
reinforcement learning
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Oya Celiktutan
King's College London United Kingdom
continual learning
reinforcement learning
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