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A General Recipe for Likelihood-free Bayesian Optimization
Jiaming Song · Lantao Yu · Willie Neiswanger · Stefano Ermon

Wed Jul 20 12:00 PM -- 12:20 PM (PDT) @ Room 327 - 329

The acquisition function, a critical component in Bayesian optimization (BO), can often be written as the expectation of a utility function under a surrogate model. However, to ensure that acquisition functions are tractable to optimize, restrictions must be placed on the surrogate model and utility function. To extend BO to a broader class of models and utilities, we propose likelihood-free BO (LFBO), an approach based on likelihood-free inference. LFBO directly models the acquisition function without having to separately perform inference with a probabilistic surrogate model. We show that computing the acquisition function in LFBO can be reduced to optimizing a weighted classification problem, which extends an existing likelihood-free density ratio estimation method related to probability of improvement (PI). By choosing the utility function for expected improvement (EI), LFBO outperforms the aforementioned method, as well as various state-of-the-art black-box optimization methods on several real-world optimization problems. LFBO can also leverage composite structures of the objective function, which further improves its regret by several orders of magnitude.

Author Information

Jiaming Song (Stanford)
Lantao Yu (Stanford University)
Willie Neiswanger (Stanford University)
Stefano Ermon (Stanford University)

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