Uncertainty In Machine Learning Professors in Global

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