Stochastic Learning Professors in Global

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Yuhong Guo
Carleton University Canada
self-supervised learning
representation learning
machine learning
semi-supervised learning
reinforcement learning
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C. Green
University of Wisconsin–Madison United States of America
human learning
motor learning
perceptual learning
depth learning
cognitive learning
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Harold Kushner
Brown University United States of America
stochastic networks
stochastic control
stochastic systems theory
complex stochastic systems
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John Oommen
Carleton University Canada
stochastic learning
stochastic automata
statistical pattern recognition
syntactic pattern recognition
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Frederi Viens
Michigan State University United States of America
stochastic control
stochastic volatility
nonlinear stochastic filtering
stochastic pdes
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Monika Eisenmann
Lund University Sweden
stochastic numerical analysis
machine learning
stochastic differential equations
randomized algorithms
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Diyora Salimova
University of Freiburg Germany
computational stochastics
machine learning
stochastic differential equations
deep learning
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Yee Teh
University of Oxford United Kingdom
probabilistic learning
machine learning
deep learning
large scale machine learning
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Zhuo Jin
Macquarie University Australia
stochastic games
stochastic optimal control
numerical methods for stochastic systems
reinforcement learning
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Mehrdad Mahdavi
Pennsylvania State University United States of America
statistical learning theory
deep learning
computational learning theory
large-scale machine learning
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Honglak Lee
University of Michigan United States of America
large-scale learning
representation learning
machine learning
deep learning
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Jason Lee
University of California, Berkeley United States of America
representation learning
machine learning
deep learning
reinforcement learning
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Pascal Vincent
University of Montreal Canada
representation learning
machine learning
deep learning
statistical models
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Linus Ericsson
University of Glasgow United Kingdom
representation learning
machine learning
automated machine learning
multi-modal learning
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Yaoliang Yu
University of Waterloo Canada
representation learning
machine learning
robust stochastic optimization
distributed learning
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Jason Lee
University of California, Berkeley United States of America
representation learning
machine learning
deep learning
reinforcement learning
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Yichen Zhang
Purdue University United States of America
multi-task learning
distributed learning
statistical models
reinforcement learning
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Rudolf Lioutikov
University of Texas at Austin United States of America
imitation learning
structure learning
sequencing
reinforcement learning
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Kavita Ramanan
Brown University United States of America
stochastic analysis
stochastic networks
stochastic processes
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Jose Figueroa-lopez
Purdue University United States of America
stochastic analysis
stochastic control
stochastic processes
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William Massey
Princeton University United States of America
stochastic analysis
stochastic networks
stochastic dominance
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