Well File:
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- Well Plat ( results)
- Wellbore Schematic ( results)
- Directional Survey ( results)
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- Log ( results)
- Density ( results)
- Gamma Ray ( results)
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- Report ( results)
- Daily Report ( results)
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- Rock Sample ( results)
Michael Pearce
Scalable Global Optimization via Local Bayesian Optimization
David Eriksson, Michael Pearce, Jacob Gardner, Ryan D. Turner, Matthias Poloczek
Bayesian optimization has recently emerged as a popular method for the sampleefficient optimization of expensive black-box functions. However, the application to high-dimensional problems with several thousand observations remains challenging, and on difficult problems Bayesian optimization is often not competitive with other paradigms. In this paper we take the view that this is due to the implicit homogeneity of the global probabilistic models and an overemphasized exploration that results from global acquisition.
Scalable Global Optimization via Local Bayesian Optimization
David Eriksson, Michael Pearce, Jacob Gardner, Ryan D. Turner, Matthias Poloczek
Bayesian optimization has recently emerged as a popular method for the sampleefficient optimization of expensive black-box functions. However, the application to high-dimensional problems with several thousand observations remains challenging, and on difficult problems Bayesian optimization is often not competitive with other paradigms. In this paper we take the view that this is due to the implicit homogeneity of the global probabilistic models and an overemphasized exploration that results from global acquisition.