Out Of Distribution Learning Professors in Global

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Yuhong Guo
Carleton University Canada
representation learning
reinforcement learning
few-shot learning
unsupervised learning
self-supervised learning
weakly supervised learning
machine learning
semi-supervised learning
continual learning
federated learning
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Yuhong Guo
Carleton University Canada
representation learning
reinforcement learning
few-shot learning
unsupervised learning
self-supervised learning
weakly supervised learning
machine learning
semi-supervised learning
continual learning
federated learning
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Pascal Poupart
University of Waterloo Canada
shot learning
reinforcement learning
causal learning
self-supervised learning
machine learning
continual learning
federated learning
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C. Green
University of Wisconsin–Madison United States of America
human learning
transfer learning
motor learning
depth learning
cognitive learning
perceptual learning
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Danica Sutherland
University of British Columbia Canada
statistical machine learning
theoretical machine learning
machine learning
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Yuri Boykov
University of Waterloo Canada
unsupervised learning
self-supervised learning
weakly supervised learning
semi-supervised learning
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Vinay Gogineni
University of Southern Denmark Denmark
self-supervised learning
machine learning
continual learning
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Christos Anagnostopoulos
University of Glasgow United Kingdom
distributed computing
machine learning
federated learning
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Salman Avestimehr
University of Southern California United States of America
distributed computing
machine learning
federated learning
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Salman Avestimehr
University of Southern California United States of America
distributed computing
machine learning
federated learning
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Salman Avestimehr
University of Southern California United States of America
distributed computing
machine learning
federated learning
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Christian Stöhr
Chalmers University of Technology Sweden
digital learning
online learning
hybrid learning
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Joseph Gonzalez
University of California, Berkeley United States of America
deep reinforcement learning
machine learning
federated learning
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Raymond Mooney
University of Texas at Austin United States of America
transfer learning
statistical relational learning
machine learning
ensemble learning
semi-supervised learning
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Laura Balzano
University of Michigan United States of America
out-of-distribution learning
machine learning
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Honglak Lee
University of Michigan United States of America
representation learning
large-scale learning
deep learning
machine learning
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Mi Park
University of British Columbia Canada
statistical machine learning
theoretical machine learning
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Xiaochen Yang
University of Glasgow United Kingdom
few-shot learning
statistical machine learning
self-supervised learning
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Cesare Alippi
Università della Svizzera italiana Switzerland
adaptation learning
machine learning
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Guang Cheng
University of California Los Angeles United States of America
statistical machine learning
deep learning theory
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Pascal Frossard
École Polytechnique Fédérale de Lausanne Switzerland
network machine learning
machine learning
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