Bayesian Uncertainty Learning Professors in Global

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Sharmistha Guha
Texas A&M University United States of America
bayesian high dimensional regression
bayesian causal inference
bayesian object-oriented regression
bayesian nonparametric mixture models
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Mehrdad Mahdavi
Pennsylvania State University United States of America
large-scale machine learning
computational statistical machine learning
computational learning theory
deep learning
statistical learning theory
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Richard Border
Carnegie Mellon University United States of America
bayesian inference
statistical learning
statistical inference
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James Beck
California Institute of Technology United States of America
bayesian updating
computational bayesian statistics
stochastic system modeling
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Jaehee Kim
Cornell University Netherlands
bayesian inference
machine learning
mathematical models
statistical inference
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Yee Teh
University of Oxford United Kingdom
bayesian statistics
bayesian nonparametrics
probabilistic learning
machine learning
deep learning
large scale machine learning
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Jason Klusowski
Princeton University United States of America
statistical learning
decision tree learning
deep learning
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Krishna Balasubramanian
University of California, Davis United States of America
statistical learning
deep learning
stochastic optimization
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Daniel Roy
University of Toronto Canada
No active positions
machine learning
deep learning
probabilistic programming
statistical learning theory
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Trevor Campbell
University of British Columbia Canada
bayesian inference
bayesian theory
bayesian nonparametrics
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Kd Dang
University of Western Australia Australia
bayesian inference
bayesian statistics
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Valen Johnson
Texas A&M University United States of America
bayesian variable selection
bayesian hypothesis testing
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Ricardo Baptista
California Institute of Technology United States of America
bayesian inference
probabilistic modeling
uncertainty quantification
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Xinran Li
University of Chicago United States of America
bayesian inference
randomization-based inference
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Debdeep Pati
University of Wisconsin–Madison United States of America
bayesian graphical models
bayesian methods
efficient bayesian computation
approximate bayesian methods
machine learning
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Hina Shaheen
University of Manitoba Canada
bayesian inference
machine learning
statistical machine learning
computational modelling
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Matthew Hole
Australian National University Australia
bayesian inference
mathematical modelling
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Sahani Pathiraja
University of New South Wales Australia
bayesian inference
machine learning
stochastic analysis
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Melih Kandemir
University of Southern Denmark Denmark
bayesian inference
machine learning
deep neural networks
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Jeremias Knoblauch
University College London United Kingdom
bayesian inference
bayesian methods
deep learning
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Tingting Zhu
University of Oxford United Kingdom
bayesian inference
machine learning
deep learning
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