BoTier: Multi-Objective Bayesian Optimization with Tiered Composite Objectives

2025-01-26

AI Summary

Multi-objective optimization in scientific experiments—e.g., jointly maximizing reaction yield while minimizing costly reagent consumption—remains challenging, especially for pedagogical accessibility to secondary students. Method: We propose a hierarchical multi-objective optimization framework with controllable complexity, centered on a differentiable tiered composite objective function that explicitly encodes objective priority hierarchies and strict nested preference constraints—overcoming expressivity limitations of conventional weighted-sum and Pareto-front approaches. Built upon the Bayesian optimization paradigm, it employs Gaussian process surrogate models and a custom differentiable acquisition function, enabling seamless integration with BoTorch via auto-differentiation. Contribution/Results: Evaluated on synthetic benchmarks and real chemical reaction surfaces, our method achieves significantly improved sample efficiency and robustly outperforms state-of-the-art multi-objective BO methods—including MOBO and EHVI—while maintaining interpretability suitable for educational contexts.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: Learning & Optimization for ROB
• Machine Learning: Optimization

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

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.

Problem

• Multi-objective Optimization
• Scientific Experiments
• Educational Complexity

Innovation

• Bayesian Optimization
• Multi-level Objective Optimization
• BoTorch Compatibility