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BORE: Bayesian Optimization by Density-Ratio Estimation
Louis Chi-Chun Tiao · Aaron Klein · Matthias W Seeger · Edwin V Bonilla · Cedric Archambeau · Fabio Ramos

Tue Jul 20 09:00 AM -- 11:00 AM (PDT) @ None #None

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 BO. In this paper, we cast the computation of EI as a binary classification problem, building on the link between class-probability estimation and density-ratio estimation, and the lesser-known link between density-ratios and EI. By circumventing the tractability constraints, this reformulation provides numerous advantages, not least in terms of expressiveness, versatility, and scalability.

Author Information

Louis Tiao (University of Sydney)
Aaron Klein (AWS Berlin)
Matthias W Seeger (Amazon)
Edwin V Bonilla (CSIRO's Data61)
Cedric Archambeau (Amazon Web Services)
Fabio Ramos (NVIDIA, University of Sydney)

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