Graph Learning Professors in Global

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C. Green
University of Wisconsin–Madison United States of America
depth learning
human learning
motor learning
cognitive learning
rate learning
transfer learning
perceptual learning
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Danai Koutra
University of Michigan United States of America
graph machine learning
graph neural networks
graph mining
graph anomaly event detection
network representation learning
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Honglak Lee
University of Michigan United States of America
deep learning
graphical models
machine learning
representation learning
large-scale learning
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Alexander Rakhlin
Massachusetts Institute of Technology United States of America
statistical learning
machine learning
reinforcement learning
online learning
neural networks
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Pieter Abbeel
University of California, Berkeley United States of America
deep learning
imitation learning
reinforcement learning
transfer learning
meta-learning
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Pascal Vincent
University of Montreal Canada
deep learning
machine learning
representation learning
pattern recognition
neural networks
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Ferenc Huszár
University of Cambridge United Kingdom
deep learning
machine learning
representation learning
transfer learning
meta-learning
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Yuhong Guo
Carleton University Canada
machine learning
representation learning
reinforcement learning
self-supervised learning
continual learning
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Pan Li
Georgia Institute of Technology United States of America
graph machine learning
graph neural networks
machine learning
geometric deep learning
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Ehsan Mobaraki
Aalborg University Denmark
graph neural networks
knowledge graphs
deep learning
neural-symbolic learning
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Abraham Bernstein
University of Zurich Switzerland
graph mining
large graph data sets
machine learning
graph stores
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Krishna Swamy
Yale University United States of America
graph theory
deep learning
machine learning
geometric deep learning
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Jason Lee
University of California, Berkeley United States of America
deep learning
machine learning
representation learning
reinforcement learning
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Jason Lee
University of California, Berkeley United States of America
deep learning
machine learning
representation learning
reinforcement learning
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Brokoslaw Laschowski
University of Toronto Canada
deep learning
machine learning
reinforcement learning
neural networks
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Fei Wang
Cornell University Netherlands
knowledge graphs
machine learning
transfer learning
meta-learning
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Andrew Casey
Monash University Australia
deep learning
machine learning
data analysis
probabilistic graphical models
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Kurt Driessens
Maastricht University Netherlands
machine learning
relational learning
reinforcement learning
transfer learning
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Yichen Zhang
Purdue University United States of America
distributed learning
reinforcement learning
transfer learning
multi-task learning
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Kurt Driessens
Maastricht University Netherlands
machine learning
relational learning
reinforcement learning
transfer learning
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Vinay Gogineni
University of Southern Denmark Denmark
machine learning
self-supervised learning
meta learning
continual learning
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