Corpus ID: 231951652

BORE: Bayesian Optimization by Density-Ratio Estimation

@article{Tiao2021BOREBO,
  title={BORE: Bayesian Optimization by Density-Ratio Estimation},
  author={Louis C. Tiao and Aaron Klein and M. Seeger and Edwin V. Bonilla and C. Archambeau and F. Ramos},
  journal={ArXiv},
  year={2021},
  volume={abs/2102.09009}
}
Bayesian optimization (BO) is among the most effective and widely-used blackbox optimization methods. BO proposes solutions according to an explore-exploit trade-off criterion encoded in an acquisition function, many of which are computed from the posterior predictive of a probabilistic surrogate model. Prevalent among these is the expected improvement (EI). The need to ensure analytical tractability of the predictive often poses limitations that can hinder the efficiency and applicability of… Expand

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