提出分层复合目标优化方法，高效平衡实验结果与参数成本
BoTier: Multi-Objective Bayesian Optimization with Tiered Composite Objectives
- 设计可分层表达实验结果与参数偏好关系的复合目标函数
- 在真实与合成数据上验证其在多目标优化中的稳健表现
- 支持自动微分，兼容BoTorch，便于科研人员直接使用
科学优化常需权衡多个竞争目标，这些目标既涉及实验结果（如最大化反应产率），也涉及输入参数（如最小化昂贵试剂用量）。实际与经济因素通常定义了这些目标的优先级层级，算法必须反映这一层次结构以实现高效的样本规划。本文提出BoTier，一种可灵活表示实验结果与输入参数间层次偏好关系的复合目标函数。通过在合成与真实场景下的系统性基准测试，验证了BoTier在多种应用场景中的鲁棒适用性。重要的是，BoTier以可自动微分的方式实现，可无缝集成至BoTorch库中，促进科学界快速采纳。
原文摘要 · Abstract (English)
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.
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