Stochastic Choice Professors in Global

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Maximilian Kasy
University of Oxford United Kingdom
statistical decision theory
algorithmic decision making
machine learning
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Monika Eisenmann
Lund University Sweden
stochastic numerical analysis
stochastic differential equations
randomized algorithms
machine learning
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Sahand Negahban
Yale University United States of America
statistical learning
statistical signal processing
machine learning
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Laurence Frank
Utrecht University Netherlands
statistical learning
predictive modelling
randomized response
machine learning
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Titouan Muzellec
University of Vienna Austria
statistical learning
predictive modeling
machine learning
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Guang Lin
Purdue University United States of America
statistical learning
predictive modeling
machine learning
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Yee Teh
University of Oxford United Kingdom
probabilistic learning
large scale machine learning
machine learning
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Hermann Ney
RWTH Aachen University Germany
statistical classification
statistical machine translation
machine learning
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Philip Protter
Columbia University United States of America
stochastic integration theory
stochastic differential equations
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Igor Vladimirov
Australian National University Australia
stochastic dynamics
stochastic robust control
quantum stochastic control
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Moritz Schauer
Chalmers University of Technology Sweden
dynamical stochastic models
stochastic differential equations
statistical inference
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Moritz Schauer
University of Gothenburg Sweden
dynamical stochastic models
stochastic differential equations
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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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Zhuo Jin
Macquarie University Australia
stochastic games
stochastic optimal control
numerical methods for stochastic systems
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Ivan Kryven
Utrecht University Netherlands
stochastic analysis
stochastic differential equations
randomized algorithms
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Xenia Miscouridou
Imperial College London United Kingdom
stochastic process modeling
statistical network modeling
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Petter Kolm
New York University United States of America
stochastic optimal control
machine learning
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Jiawei Li
University of Edinburgh United Kingdom
probabilistic stochastic methods
stochastic partial differential equations
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Debmalya Mandal
University of Warwick United Kingdom
computational social choice
machine learning
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Luhao Zhang
Johns Hopkins University United States of America
stochastic robust optimization
machine learning
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Ivan Corwin
Columbia University United States of America
stochastic growth models
probabilistic models
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