BoTier: Multi-Objective Bayesian Optimization with Tiered Composite Objectives

TL;DR

BoTier introduces a flexible, hierarchical composite objective for multi-objective Bayesian optimization, effectively balancing experimental outcomes and input preferences, with broad applicability and easy integration into existing tools.

Contribution

It presents BoTier, a novel hierarchical composite objective for Bayesian optimization, and demonstrates its robustness and seamless integration with BoTorch for scientific applications.

Findings

Effective on synthetic and real-life surfaces

Robust across diverse use cases

Easily integrated with BoTorch

Abstract

Scientific optimization problems are usually concerned with balancing multiple competing objectives, which come as preferences over both the outcomes of an experiment (e.g. maximize the reaction yield) and the corresponding input parameters (e.g. minimize the use of an expensive reagent). Typically, practical and economic considerations define a hierarchy over these objectives, which must be reflected in algorithms for sample-efficient experiment planning. Herein, we introduce BoTier, a composite objective that can flexibly represent a hierarchy of preferences over both experiment outcomes and input parameters. We provide systematic benchmarks on synthetic and real-life surfaces, demonstrating the robust applicability of BoTier across a number of use cases. Importantly, BoTier is implemented in an auto-differentiable fashion, enabling seamless integration with the BoTorch library, thereby facilitating adoption by the scientific community.