Bayesian Optimization Professors in Global

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Kalyanmoy Deb
Michigan State University United States of America
computational optimization
multi-objective optimization
process optimization
evolutionary multi-objective optimization
hybrid optimization algorithms
machine learning
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Vijaya Raghavendra Bollapragada
University of Texas at Austin United States of America
stochastic optimization
optimization algorithms
constrained optimization
nonlinear optimization
machine learning
distributed optimization
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Debdeep Pati
University of Wisconsin–Madison United States of America
bayesian methods
efficient bayesian computation
bayesian graphical models
approximate bayesian methods
machine learning
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Albert Berahas
University of Michigan United States of America
stochastic optimization
machine learning optimization
constrained optimization
nonlinear optimization
distributed optimization
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Merve Bodur
University of Edinburgh United Kingdom
combinatorial optimization
multiobjective optimization
machine learning
robust optimization
inverse optimization
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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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Navish Kumar
University of Basel Switzerland
bayesian inference
riemannian optimization
optimization
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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Shixuan Zhang
Texas A&M University United States of America
stochastic optimization
polynomial optimization
integer optimization
robust optimization
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Cynthia Vinzant
University of Washington United States of America
mathematical optimization
combinatorial optimization
linear optimization
discrete optimization
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Nathan Kallus
Cornell University Netherlands
stochastic optimization
optimization
machine learning
robust optimization
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Bartolomeo Stellato
Princeton University United States of America
mathematical optimization
large scale optimization
machine learning
differentiable optimization
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Nathaniel Bastian
Pennsylvania State University United States of America
computational stochastic optimization
optimization
machine learning
multiple objective optimization
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Jeya Jeyakumar
University of New South Wales Australia
mathematical optimization
nonlinear optimization
machine learning
robust optimization
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Phebe Vayanos
University of Southern California United States of America
stochastic optimization
machine learning
integer optimization
robust optimization
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Jelena Diakonikolas
University of Wisconsin–Madison United States of America
large-scale optimization
machine learning
first-order optimization algorithms
non-convex optimization
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Chuan He
Linköping University Sweden
large-scale optimization
continuous optimization
machine learning
decentralized optimization
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Reem Khir
Purdue University United States of America
optimization
robust stochastic optimization
constrained optimization
machine learning
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Yaoliang Yu
University of Waterloo Canada
optimization algorithms
robust stochastic optimization
machine learning
non-convex optimization
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John Chinneck
Carleton University Canada
optimization
feasibility optimization
nonlinear optimization
mixed integer optimization
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Juan Vera Lizcano
Tilburg University Netherlands
combinatorial optimization
polynomial copositive optimization
machine learning
convergence of optimization algorithms
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