Bayesian Model Optimization Professors in Global

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Kalyanmoy Deb
Michigan State University United States of America
computational optimization
meta-modeling optimization
multi-objective optimization
evolutionary multi-objective optimization
hybrid optimization algorithms
process optimization
machine learning
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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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Vijaya Raghavendra Bollapragada
University of Texas at Austin United States of America
stochastic optimization
optimization algorithms
nonlinear optimization
constrained optimization
machine learning
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Yanan Fan
University of New South Wales Australia
bayesian computation
bayesian inference
approximate bayesian computation
bayesian nonparametrics
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Mayank Chadha
University of California, San Diego United States of America
bayesian optimization
bayesian decision theory
geometrically exact modeling
machine learning
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Sharmistha Guha
Texas A&M University United States of America
bayesian high dimensional regression
bayesian object-oriented regression
bayesian causal inference
bayesian nonparametric mixture models
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Albert Berahas
University of Michigan United States of America
machine learning optimization
stochastic optimization
nonlinear optimization
constrained optimization
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Navish Kumar
University of Basel Switzerland
bayesian inference
riemannian optimization
optimization
machine learning
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Bartolomeo Stellato
Princeton University United States of America
mathematical optimization
large scale optimization
differentiable optimization
machine learning
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Nathaniel Bastian
Pennsylvania State University United States of America
computational stochastic optimization
optimization
multiple objective optimization
machine learning
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Reem Khir
Purdue University United States of America
robust stochastic optimization
optimization
constrained optimization
machine learning
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Juergen Branke
Warwick Business School United Kingdom
bayesian optimization
simulation-based optimization
multi-objective optimization
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Jaroslav Fowkes
University of Oxford United Kingdom
bayesian optimization
optimization
machine learning
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Trevor Campbell
University of British Columbia Canada
bayesian inference
bayesian theory
bayesian nonparametrics
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Umberto Picchini
University of Gothenburg Sweden
bayesian computational methods
approximate bayesian computation
stochastic modelling
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Umberto Picchini
Chalmers University of Technology Sweden
bayesian computational methods
approximate bayesian computation
stochastic modelling
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Edson Utazi
University of Southampton United Kingdom
bayesian computation
bayesian inference
machine learning
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Cynthia Vinzant
University of Washington United States of America
mathematical optimization
combinatorial optimization
linear optimization
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Robin Ryder
Imperial College London United Kingdom
bayesian computational statistics
approximate bayesian computation
stochastic modelling
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Paul Arora
University of Toronto Canada
bayesian networks
bayesian statistics
machine learning
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Alexander Hartemink
Duke University Singapore
bayesian networks
bayesian statistics
machine learning
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