Variance Reduction Professors in Global

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Laurence Frank
Utrecht University Netherlands
randomized response
predictive modelling
machine learning
statistical learning
multivariate statistics
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Yaoliang Yu
University of Waterloo Canada
robust stochastic optimization
machine learning
representation learning
generative modelling
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Yuhong Guo
Carleton University Canada
machine learning
representation learning
reinforcement learning
weakly supervised learning
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A.c.c. Coolen
Radboud University Netherlands
machine learning
statistical inference
predictive analysis
random networks
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Sara Wade
University of Edinburgh United Kingdom
variational inference
dimension reduction
machine learning
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Joshua Loftus
London School of Economics United Kingdom
bias reduction
machine learning
statistical inference
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Jan Bauer
Vrije Universiteit Amsterdam Netherlands
dimensionality reduction
variable selection
statistical learning
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Fangxin Fang
Imperial College London United Kingdom
model reduction
predictive modeling
machine learning
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Xiaofei Shi
University of Toronto Canada
dimensionality reduction
machine learning
stochastic optimization
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Francesca Chiaromonte
Pennsylvania State University United States of America
regression
dimension reduction
multivariate methods
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Inge Koch
University of Western Australia Australia
dimension reduction
multivariate data analysis
statistical learning
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Wei-biao Wu
University of Chicago United States of America
regression
dependent random variables
mean vector estimation
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Professorship Biometry Molecular Medicine
Ludwig Maximilian University of Munich Germany
variable selection
machine learning
prediction modeling
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Victor Panaretos
École Polytechnique Fédérale de Lausanne Switzerland
regression modeling
statistical inference
multivariate statistics
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Bojana Rosic
University of Twente Netherlands
reduced modeling
machine learning
statistical inference
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Rayjean Hung
University of Toronto Canada
risk prediction
machine learning
statistical modeling
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Jaime Ide
Yale University United States of America
predictive modeling
machine learning
pattern recognition
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Raaz Dwivedi
Cornell University Netherlands
random sampling
reinforcement learning
statistical machine learning
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Pietro Perona
California Institute of Technology United States of America
visual recognition
machine learning
machine vision
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Jason Lee
University of California, Berkeley United States of America
machine learning
representation learning
reinforcement learning
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Pascal Vincent
University of Montreal Canada
machine learning
representation learning
pattern recognition
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