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Professors
Uncertainty In Machine Learning
Uncertainty In Machine Learning Professors in Global
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Professor
Samir Maity
Aalborg University
Denmark
uncertainty quantification
imprecise modeling
machine learning
deep learning
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Professor
Yingzhen Li
Imperial College London
United Kingdom
uncertainty quantification
probabilistic machine learning
machine learning
explainable machine learning
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Professor
Justin Beland
University of Toronto
Canada
optimization under uncertainty
machine intelligence
probabilistic modeling
deep learning
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Professor
Saikat Chatterjee
Royal Institute of Technology
Sweden
statistical machine learning
data inference
deep learning
explainable machine learning
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Professor
Michele Caprio
University of Manchester
United Kingdom
uncertainty quantification
imprecise probabilistic machine learning
machine learning
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Professor
Cheng Ouyang
University of Oxford
United Kingdom
uncertainty modeling
trustworthy machine learning
multimodal machine learning
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Professor
Magnus Ross
University College London
United Kingdom
uncertainty quantification
probabilistic machine learning
machine learning
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Professor
Geoff Pleiss
University of British Columbia
Canada
uncertainty quantification
probabilistic machine learning
machine learning
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Professor
Robert Gracie
University of Waterloo
Canada
uncertainty
machine learning
deep learning
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Professor
Dirk Husmeier
University of Glasgow
United Kingdom
uncertainty quantification
machine learning
parameter inference
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Professor
Agustinus Kristiadi
Western University
Canada
uncertainty quantification
probabilistic machine learning
deep learning
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Professor
Ben Adcock
Simon Fraser University
Canada
uncertainty quantification
machine learning
deep learning
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Professor
John Quilty
University of Waterloo
Canada
uncertainty quantification
machine learning
deep learning
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Professor
Bojana Rosic
University of Twente
Netherlands
uncertainty quantification
machine learning
statistical inference
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Professor
Xiaowu Dai
University of California Los Angeles
United States of America
uncertainty quantification
machine learning
statistical inference
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Professor
James Kermode
University of Warwick
United Kingdom
quantifying uncertainty
machine learning
predictive modelling
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Professor
Geoff Pleiss
University of British Columbia
Canada
uncertainty quantification
probabilistic modeling
deep learning
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Professor
Bahar Taskesen
University of Chicago Booth School of Business
United States of America
decision-making under uncertainty
machine learning
statistical inference
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Professor
Shai Ben-david
University of Waterloo
Canada
reliability in machine learning
interpretability in machine learning
machine learning
fairness in machine learning
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Professor
Danica Sutherland
University of British Columbia
Canada
machine learning
statistical machine learning
theoretical machine learning
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Professor
Henrik Boström
KTH Royal Institute of Technology
Sweden
probabilistic machine learning
trustworthy machine learning
explainable machine learning
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