Robust Interpretable Learning Professors in Global

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Yuhong Guo
Carleton University Canada
reinforcement learning
representation learning
self-supervised learning
continual learning
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Cassio De Campos
Eindhoven University of Technology Belgium
robust artificial intelligence
reliable machine learning
statistical learning
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Yaoliang Yu
University of Waterloo Canada
robust stochastic optimization
representation learning
robustness
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Randall Balestriero
Brown University United States of America
learnable signal processing
representation learning
self-supervised learning
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C. Green
University of Wisconsin–Madison United States of America
perceptual learning
human learning
cognitive learning
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Yinbei Li
Singapore Institute of Technology Singapore
reinforcement learning
imitation learning
robot learning
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Rudolf Lioutikov
University of Texas at Austin United States of America
reinforcement learning
imitation learning
structure learning
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Linus Ericsson
University of Glasgow United Kingdom
representation learning
robustness
multi-modal learning
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Muhammad Hafez
University of Southampton United Kingdom
reinforcement learning
robot learning
continual robot learning
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Natalie Bennett
Monash University Australia
constructivist learning
collaborative learning
blended learning
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Benedek Kurdi
University of Illinois United States of America
reinforcement learning
propositional learning
causal learning
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Rupam Mahmood
University of Alberta Canada
reinforcement learning
real-time learning systems
continual learning
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Kevin Luck
Vrije Universiteit Amsterdam Netherlands
reinforcement learning
robot learning
deep reinforcement learning
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Jessica Lee
University of Sydney Australia
causal learning
rule learning
associative learning
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Zihuai University
Stanford University United States of America
interpretable machine learning
explainable machine learning
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Michael Guerzhoy
University of Toronto Canada
interpretable machine learning
mechanistic interpretability
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Amir Rahnama
KTH Royal Institute of Technology Sweden
interpretable machine learning
explainable machine learning
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Yan Leng
University of Texas at Austin United States of America
interpretable machine learning
human-centric interpretability
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Flora Salim
University of New South Wales Australia
robust machine learning
trustworthy machine learning
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David Inouye
Purdue University United States of America
robust machine learning
trustworthy machine learning
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Huan Yu
University of Southampton United Kingdom
robust optimization
reinforcement learning
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