Likelihood Inference Professors in Global

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George Azzopardi
University of Groningen Netherlands
machine learning
data mining
predictive analysis
pattern recognition
deep learning
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Rajesh Ranganath
New York University United States of America
probabilistic inference
statistical inference
causal inference
machine learning
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Abhishek Chakrabortty
Texas A&M University United States of America
statistical inference
bayesian inference
causal inference
machine learning
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Dae Ham
University of Michigan United States of America
randomization inference
causal inference
adaptive sequential inference
design-based causal inference
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Raaz Dwivedi
Cornell University Netherlands
bayesian inference
causal inference
statistical machine learning
random sampling
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Irina Rish
University of Montreal Canada
probabilistic inference
machine learning
deep learning
biologically plausible reinforcement learning
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Mahdi Soltanolkotabi
University of Southern California United States of America
statistical inference
machine learning
deep learning
high-dimensional probability statistics
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A.c.c. Coolen
Radboud University Netherlands
statistical inference
machine learning
predictive analysis
neural networks
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Tingting Zhu
University of Oxford United Kingdom
bayesian inference
machine learning
probabilistic techniques
deep learning
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Zoltán Szabó
London School of Economics and Political Science United Kingdom
bayesian inference
statistical machine learning
shape-constrained prediction
distribution regression
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Andrew Casey
Monash University Australia
inference
machine learning
deep learning
probabilistic graphical models
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Tom Rainforth
University of Oxford United Kingdom
approximate inference
statistical machine learning
probabilistic programming
deep learning
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Mogana Ganggayah
Monash University Australia
machine learning
predictive modeling
data mining
deep learning
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Gerasimos Spanakis
Maastricht University Netherlands
machine learning
data mining
deep learning
neural networks
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Nanwei Wang
University of New Brunswick Canada
machine learning
scalable likelihood methods
composite likelihood
bayesian model selection
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Mahdi Khodayar
The University of Tulsa Iran
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machine learning
statistical pattern recognition
deep neural networks
probabilistic graphical models
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Pascal Vincent
University of Montreal Canada
machine learning
pattern recognition
deep learning
neural networks
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Xinran Li
University of Chicago United States of America
randomization-based inference
bayesian inference
causal inference
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Torkel Erhardsson
Linköping University Sweden
statistical inference
bayesian inference
probability theory
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Jaehee Kim
Cornell University Netherlands
statistical inference
bayesian inference
machine learning
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Victor Veitch
University of Chicago United States of America
statistical inference
causal inference
machine learning
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