Stochastic Learning Professors in Global

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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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Rudolf Lioutikov
University of Texas at Austin United States of America
imitation learning
structure learning
sequencing
reinforcement learning
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Pascal Poupart
University of Waterloo Canada
self-supervised learning
machine learning
causal learning
reinforcement learning
shot learning
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Diyora Salimova
University of Freiburg Germany
computational stochastics
machine learning
stochastic differential equations
deep learning
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Moritz Schauer
Chalmers University of Technology Sweden
dynamical stochastic models
stochastic differential equations
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Moritz Schauer
University of Gothenburg Sweden
dynamical stochastic models
stochastic differential equations
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Mehrdad Mahdavi
Pennsylvania State University United States of America
statistical computational learning theory
high-dimensional learning
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Antoine Van Den Beemt
Eindhoven University of Technology Belgium
networked learning
learning analytics
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Alessandro Abate
University of Oxford United Kingdom
stochastic hybrid systems
data-driven learning algorithms
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Kostas Kalogeropoulos
London School of Economics and Political Science United Kingdom
stochastic differential equations
stochastic epidemic models
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Reza Skandari
Imperial College London United Kingdom
stochastic dynamic optimization
reinforcement learning
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Igor Vladimirov
Australian National University Australia
stochastic dynamics
stochastic robust control
quantum stochastic control
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Iro Laina
University of Oxford United Kingdom
self-supervised learning
language-supervised learning
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Amir Taghvaei
University of Washington United States of America
stochastic thermodynamics
machine learning
reinforcement learning
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George Shanthikumar
Purdue University United States of America
stochastic processes simulation
model uncertainty 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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Yuhong Guo
Carleton University Canada
self-supervised learning
representation learning
machine learning
semi-supervised learning
reinforcement learning
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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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Wael Suleiman
University of Sherbrooke Canada
imitation learning
reinforcement learning
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Matthew Howard
King's College London United Kingdom
imitation learning
machine learning
reinforcement learning
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Yinbei Li
Singapore Institute of Technology Singapore
robot learning
imitation learning
reinforcement learning
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