Bayesian Optimization of Function Networks with Partial Evaluations
Poompol Buathong Jiayue Wan Samuel Daulton Raul Astudillo Maximilian Balandat Peter I. Frazier
Bayesian optimization is a framework for optimizing functions that are costly
or time-consuming to evaluate. Recent work has considered Bayesian optimization
of function networks (BOFN), where the objective function is computed via a
network of functions, each taking as input the output of previous nodes in the
network and additional parameters. Exploiting this network structure has been
shown to yield significant performance improvements. Existing BOFN algorithms
for general-purpose networks are required to evaluate the full network at each
iteration. However, many real-world applications allow evaluating nodes
individually. To take advantage of this opportunity, we propose a novel
knowledge gradient acquisition function for BOFN that chooses which node to
evaluate as well as the inputs for that node in a cost-aware fashion. This
approach can dramatically reduce query costs by allowing the evaluation of part
of the network at a lower cost relative to evaluating the entire network. We
provide an efficient approach to optimizing our acquisition function and show
it outperforms existing BOFN methods and other benchmarks across several
synthetic and real-world problems. Our acquisition function is the first to
enable cost-aware optimization of a broad class of function networks.