Resource Efficient Machine Learning Professors in Global

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Ruth Misener
Imperial College London United Kingdom
machine learning
robust optimization
computational optimization
data-driven optimization
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Abdullah Alelyani
University of Western Australia Australia
machine learning
energy use efficiency
scheduling optimization
deep learning
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Nathan Kallus
Cornell University Netherlands
machine learning
robust optimization
optimization
stochastic optimization
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Kilian Weinberger
Cornell University Netherlands
resource efficient learning
machine learning
deep learning
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Jiaqi Gu
Arizona State University China
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efficient algorithm
energy-efficient computing
efficient ai hardware design
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Danica Sutherland
University of British Columbia Canada
machine learning
statistical machine learning
theoretical machine learning
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Slobodan Mitrovic
University of California, Davis United States of America
efficient algorithms
optimization
memory-constrained computation
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Pascal Frossard
École Polytechnique Fédérale de Lausanne Switzerland
machine learning
network machine learning
optimization
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Samy Bengio
École Polytechnique Fédérale de Lausanne Switzerland
machine learning
statistical machine learning
deep learning
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Fabricio Oliveira
Technical University of Denmark Denmark
machine learning
robust optimization
optimization
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Dick Den Hertog
University of Amsterdam Netherlands
machine learning
robust optimization
linear optimization
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Reem Khir
Purdue University United States of America
machine learning
robust stochastic optimization
constrained optimization
optimization
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Phebe Vayanos
University of Southern California United States of America
machine learning
robust optimization
stochastic optimization
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Phebe Vayanos
University of Southern California United States of America
machine learning
robust optimization
stochastic optimization
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Phebe Vayanos
University of Southern California United States of America
machine learning
robust optimization
stochastic optimization
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Kalyanmoy Deb
Michigan State University United States of America
machine learning
computational optimization
process optimization
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Geoff Pleiss
University of British Columbia Canada
machine learning
reliable deep learning
probabilistic machine learning
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Daniel Kuhn
École Polytechnique Fédérale de Lausanne Switzerland
machine learning
robust optimization
data-driven optimization
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Yaoliang Yu
University of Waterloo Canada
machine learning
robust stochastic optimization
optimization algorithms
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Jessica Sorrell
Johns Hopkins University United States of America
machine learning
machine learning algorithms
machine learning theory
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Molei Tao
Georgia Institute of Technology United States of America
machine learning
probabilistic machine learning
optimization
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