BoTier: Multi-Objective Bayesian Optimization with Tiered Composite Objectives
This addresses sample-efficient experiment planning for scientific optimization problems, but it is incremental as it builds on existing Bayesian optimization frameworks.
The paper tackles the problem of balancing multiple competing objectives in scientific optimization by introducing BoTier, a composite objective that represents a hierarchy of preferences over experiment outcomes and input parameters, demonstrating robust applicability on synthetic and real-life benchmarks.
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.