Deep Learning Theory Professors in Global

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Mehrdad Mahdavi
Pennsylvania State University United States of America
deep learning
computational learning theory
statistical learning theory
large-scale machine learning
computational statistical machine learning
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Gerasimos Spanakis
Maastricht University Netherlands
deep learning
machine learning
neural networks
computational modeling
data mining
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Brokoslaw Laschowski
University of Toronto Canada
deep learning
machine learning
neural networks
reinforcement learning
neural computation
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Semih Cayci
RWTH Aachen University Germany
deep learning theory
machine learning
optimization theory
reinforcement learning
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Yee Teh
University of Oxford United Kingdom
deep learning
machine learning
large scale machine learning
probabilistic learning
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Daniel Roy
University of Toronto Canada
No active positions
deep learning
machine learning
statistical learning theory
decision theory
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Yarin Gal
University of Oxford United Kingdom
machine learning
deep reinforcement learning
bayesian deep learning
deep probabilistic ai
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Tahereh Koohi
University of New South Wales Australia
deep learning
machine learning
neural networks
evolutionary algorithms
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Sriram Subramanian
Carleton University Canada
deep learning
machine learning
game theory
reinforcement learning
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Fotios Kokkotos
Boston University United States of America
deep learning
machine learning
sampling theory
data mining
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Kevin Luck
Vrije Universiteit Amsterdam Netherlands
deep learning
deep reinforcement learning
robot learning
reinforcement learning
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Yuxin Chen
University of Pennsylvania United States of America
machine learning theory
information theory
statistical machine learning
reinforcement learning
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Melih Kandemir
University of Southern Denmark Denmark
deep neural networks
machine learning
reinforcement learning
bayesian inference
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Eilyan Bitar
Cornell University Netherlands
machine learning
game theory
information theory
control theory
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Nihar Shah
Carnegie Mellon University United States of America
machine learning
game theory
information theory
statistical learning
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Pawel Herman
KTH Royal Institute of Technology Sweden
machine learning
deep architectures
probability theory
artificial neural networks
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Jun Zhang
University of Michigan United States of America
neural network theory
game theory
computational modeling
reinforcement learning
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Cai Kui
Singapore University of Technology and Design Singapore
machine learning
information theory
communication theory
coding theory
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Qing Zhao
Cornell University Netherlands
machine learning
machine learning systems
information theory
sequential decision theory
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Alexander Rakhlin
Massachusetts Institute of Technology United States of America
machine learning
neural networks
reinforcement learning
statistical learning
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Kevin Tang
Cornell University Netherlands
game theory
information theory
optimization theory
control theory
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