发表机构
Graduate School of Science and Technology, Keio University; Department of Biosciences and Informatics, Keio University（庆应义塾大学理工学研究科; 庆应义塾大学生物科学与信息学系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对实际实验科学中传统贝叶斯优化收敛性差的问题，开发PolyBO方法，利用自适应更新的通用参数模型生成高质量伪实验数据，结合实验数据更新代理模型，有效减少优化时间，在合成函数和实际问题中均有显著效果。
AI 中文摘要
贝叶斯优化（BO）是一种通过平衡探索和利用来顺序提出下一个候选可解释变量以优化目标变量的优化方法，常用于深度学习超参数调整等有限评估预算场景。传统BO在实际实验科学中可能收敛性差。近期虽有利用伪实验数据加速优化的BO方法，但实验数据有限时，生成的伪实验数据质量可能不足。本研究开发了PolyBO，通过生成高质量伪实验数据改善优化时间。它用自适应更新的通用参数模型生成伪实验数据，用实验数据和伪实验数据的组合数据集更新BO代理模型后进行优化。使用合成基准函数和实际材料成分优化问题实验发现，PolyBO分别将优化时间中位数减少了42%和96%，在每个实验耗时久的场景中实现了高效优化。
英文摘要
Bayesian optimization (BO) is an optimization method that sequentially proposes the next candidate explainable variables for optimizing target variables by balancing exploration and exploitation. BO is often used under a limited evaluation budget, such as hyperparameter tuning of deep learning. Despite its effectiveness, conventional BO may have poor convergence in practical experimental science where each evaluation is often costly and time-consuming. Recently, BO methods have been proposed that accelerate optimization by using pseudo-experimental data that simulate experimental data. However, when only a limited number of experimental data are available, the generated pseudo-experimental data may be of insufficient quality. In this study, we developed PolyBO to improve optimization time by generating high-quality pseudo-experimental data even when the number of trials is limited. PolyBO performs BO efficiently by generating pseudo-experimental data with an adaptively updated versatile parametric model. This low-capacity polynomial regression model is intended to enable efficient BO even with limited experimental data. PolyBO updates the BO surrogate model with a combined dataset consisting of experimental data and pseudo-experimental data and then performs optimization. Using synthetic benchmark functions with diverse landscapes, we found that PolyBO reduced the optimization time by a median of 42\%. For a real-world material composition optimization problem, PolyBO reduced the optimization time by a median of 96\% compared with conventional methods. Overall, PolyBO achieves efficient optimization in settings where each experiment requires a long time.
