Bayesian Federated 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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Debdeep Pati
University of Wisconsin–Madison United States of America
bayesian graphical models
bayesian methods
efficient bayesian computation
machine learning
approximate bayesian methods
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Yee Teh
University of Oxford United Kingdom
bayesian statistics
machine learning
probabilistic learning
bayesian nonparametrics
large scale machine learning
deep learning
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Jun Zhang
University of Michigan United States of America
bayesian decision theory
reinforcement learning
computational modeling
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Tal Kachman
Radboud University Netherlands
machine learning
reinforcement learning
deep learning
financial machine learning
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Tommy Löfstedt
Umeå University Sweden
bayesian federated learning
machine learning
reinforcement learning
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Xenia Miscouridou
Imperial College London United Kingdom
bayesian statistical machine learning
bayesian nonparametrics
deep learning
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Valen Johnson
Texas A&M University United States of America
bayesian variable selection
bayesian hypothesis testing
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Qifan Song
Purdue University United States of America
bayesian data science
machine learning
deep learning
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Mateus Maia Marques
University of Glasgow United Kingdom
bayesian tree models
statistical learning models
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James Beck
California Institute of Technology United States of America
computational bayesian statistics
bayesian updating
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Chang Sun
Maastricht University Netherlands
federated machine learning
deep learning
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Nathaniel Bastian
Pennsylvania State University United States of America
federated machine learning
machine learning
deep learning
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Nhat Ho
University of Texas at Austin United States of America
bayesian statistics
statistical learning
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Feng Liang
University of Illinois United States of America
bayesian statistics
statistical learning
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Yuki Shiraito
University of Michigan United States of America
bayesian statistical models
large-scale computational algorithms
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Andrew Casey
Monash University Australia
bayesian statistics
machine learning
deep learning
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Yixin Wang
University of Michigan United States of America
bayesian statistics
machine learning
probabilistic generative modeling
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Varun Kanade
University of Oxford United Kingdom
computational learning
deep learning
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Quan Zhou
Texas A&M University United States of America
bayesian methodology
iterative algorithms
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Anthony Coache
Imperial College London United Kingdom
reinforcement learning
statistical learning
stochastic modeling
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