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Article overview
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A General Framework for Constrained Bayesian Optimization using Information-based Search | | José Miguel Hernández-Lobato
; Michael A. Gelbart
; Ryan P. Adams
; Matthew W. Hoffman
; Zoubin Ghahramani
; | | Date: |
30 Nov 2015 | | Abstract: | We present an information-theoretic framework for solving global black-box
optimization problems that also have black-box constraints. Of particular
interest to us is to efficiently solve problems with decoupled constraints, in
which subsets of the objective and constraint functions may be evaluated
independently. For example, when the objective is evaluated on a CPU and the
constraints are evaluated independently on a GPU. These problems require an
acquisition function that can be separated into the contributions of the
individual function evaluations. We develop one such acquisition function and
call it Predictive Entropy Search with Constraints (PESC). PESC is an
approximation to the expected information gain criterion and it compares
favorably to alternative approaches based on improvement in several synthetic
and real-world problems. In addition to this, we consider problems with a mix
of functions that are fast and slow to evaluate. These problems require
balancing the amount of time spent in the meta-computation of PESC and in the
actual evaluation of the target objective. We take a bounded rationality
approach and develop a partial update for PESC which trades off accuracy
against speed. We then propose a method for adaptively switching between the
partial and full updates for PESC. This allows us to interpolate between
versions of PESC that are efficient in terms of function evaluations and those
that are efficient in terms of wall-clock time. Overall, we demonstrate that
PESC is an effective algorithm that provides a promising direction towards a
unified solution for constrained Bayesian optimization. | | Source: | arXiv, 1511.9422 | | Services: | Forum | Review | PDF | Favorites | |
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