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We consider Bayesian optimization of an expensive-to-evaluate black-box objective function, where we also have access to cheaper approximations of the objective.
Combining probability distributions from dependent information sources
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Efficient Global Optimization of Expensive Black-Box Functions
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Predicting the output from a complex computer code when fast approximations are available
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The Knowledge Gradient Policy for Correlated Normal Beliefs
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J. Villemonteix, E. Vazquez, and E. Walter · 2009
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The correlated knowledge gradient for simulation optimization of continuous parameters using gaussian process regression
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Provably convergent multifidelity optimization algorithm not requiring high-fidelity derivatives
Cokriging-based sequential design strategies using fast cross-validation techniques for multi-fidelity computer codes
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Multifidelity optimization using statistical surrogate modeling for non-hierarchical information sources
R. Lam, D. Allaire, and K. Willcox · 2015
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Multi-fidelity gaussian process bandit optimisation
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Multi-task bayesian optimization
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A mathematical and computational framework for multifidelity design and analysis with computer models
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Predictive entropy search for efficient global optimization of black-box functions
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Sequential kriging optimization using multiple-fidelity evaluations
D. Huang, T. Allen, W. Notz, and R. Miller
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Global Optimization of Stochastic Black-Box Systems via Sequential Kriging Meta-Models
D. Huang, T. T. Allen, W. I. Notz, and N. Zeng
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