Corpus ID: 202769642

Scalable Global Optimization via Local Bayesian Optimization

@inproceedings{Eriksson2019ScalableGO,
  title={Scalable Global Optimization via Local Bayesian Optimization},
  author={David Eriksson and Michael Pearce and Jacob R. Gardner and Ryan D. Turner and M. Poloczek},
  booktitle={NeurIPS},
  year={2019}
}
Bayesian optimization has recently emerged as a popular method for the sample-efficient 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… Expand
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