Continual Machine Learning Professors in Global

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Mehrdad Mahdavi
Pennsylvania State University United States of America
computational statistical machine learning
large-scale machine learning
deep learning
computational learning theory
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Yisong Yue
California Institute of Technology United States of America
statistical machine learning
machine learning
structured machine learning
interactive machine learning
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Molei Tao
Georgia Institute of Technology United States of America
machine learning
probabilistic machine learning
dynamical systems machine learning
deep learning theory
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Tal Kachman
Radboud University Netherlands
machine learning
deep learning
financial machine learning
reinforcement learning
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Helene Thomsen
Aarhus University Denmark
machine learning
deep learning
continuous glucose monitoring
prediction modeling
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Geoff Webb
Monash University Australia
machine learning
interactive machine learning
ensemble machine learning
bayesian machine learning
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Vinay Gogineni
University of Southern Denmark Denmark
continual learning
machine learning
quantum machine learning
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Dong Gong
University of New South Wales Australia
continual learning
machine learning
deep learning
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Adam White
University of Alberta Canada
continual learning
machine learning
reinforcement learning
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Irina Rish
University of Montreal Canada
continual lifelong learning
machine learning
deep learning
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Yuhong Guo
Carleton University Canada
continual learning
machine learning
reinforcement learning
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Oya Celiktutan
King's College London United Kingdom
continual learning
multimodal machine learning
reinforcement learning
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Hina Shaheen
University of Manitoba Canada
statistical machine learning
machine learning
computational modelling
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Chuan He
Linköping University Sweden
continuous optimization
machine learning
deep learning
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Holger Rauhut
Ludwig-Maximilians-Universität München Germany
continuous optimization
machine learning
deep learning
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Hamid Alinejad-rokny
University of New South Wales Australia
statistical machine learning
machine learning
biomedical machine learning
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Katya Scheinberg
Georgia Institute of Technology United States of America
continuous optimization
machine learning
reinforcement learning
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Yinyu Ye
Stanford University United States of America
continuous optimization
machine learning
reinforcement learning
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Yee Teh
University of Oxford United Kingdom
machine learning
large scale machine learning
deep learning
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Adam Sobey
University of Southampton United Kingdom
continuous environments
machine learning
reinforcement learning
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Yisong Yue
California Institute of Technology United States of America
statistical machine learning
structured machine learning
interactive machine learning
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