Fast Bayesian Optimization of Function Networks with Partial Evaluations

TL;DR

This paper introduces an accelerated Bayesian optimization method for function networks with partial evaluations, significantly reducing computational overhead while maintaining query efficiency, beneficial for applications like manufacturing and drug discovery.

Contribution

We propose a new accelerated p-KGFN algorithm that decreases computational costs using global Monte Carlo simulations, with minimal impact on query efficiency.

Findings

Achieves up to 16x speedup over original p-KGFN

Maintains competitive query efficiency

Reduces computational overhead significantly

Abstract

Bayesian optimization of function networks (BOFN) is a framework for optimizing expensive-to-evaluate objective functions structured as networks, where some nodes' outputs serve as inputs for others. Many real-world applications, such as manufacturing and drug discovery, involve function networks with additional properties - nodes that can be evaluated independently and incur varying costs. A recent BOFN variant, p-KGFN, leverages this structure and enables cost-aware partial evaluations, selectively querying only a subset of nodes at each iteration. p-KGFN reduces the number of expensive objective function evaluations needed but has a large computational overhead: choosing where to evaluate requires optimizing a nested Monte Carlo-based acquisition function for each node in the network. To address this, we propose an accelerated p-KGFN algorithm that reduces computational overhead with only a modest loss in query efficiency. Key to our approach is generation of node-specific candidate inputs for each node in the network via one inexpensive global Monte Carlo simulation. Numerical experiments show that our method maintains competitive query efficiency while achieving up to a 16x speedup over the original p-KGFN algorithm.