tidyHEBO提升贝叶斯优化在噪声和非平稳问题中的鲁棒性，适合实验室实验优化。
Modernizing HEBO: a robust Bayesian optimization baseline for practical heteroskedastic and non-stationary problems
- 重构HEBO思想，改进代理模型训练与输出变换策略
- 在合成函数、材料搜索等任务中表现优于或媲美现有方法
- 特别适合噪声大、动态变化的实验场景，是可靠的基准工具
贝叶斯优化被广泛应用于化学、材料科学等实验场景以实现数据高效探索，但其实际性能高度依赖代理模型假设是否匹配目标函数的几何结构与噪声特性。本文提出tidyHEBO，一个受异方差进化贝叶斯优化（HEBO）启发的鲁棒单目标序列优化框架，基于BoTorch重构了原始HEBO的设计理念，并改进了代理模型训练、输出变换选择、采集函数评估及帕累托前沿搜索等环节。我们在合成函数、Olympus模拟器、全实验反应优化数据集、针堆找针（NIAH）材料问题以及Bayesmark超参数优化任务上进行了基准测试。结果表明，tidyHEBO在多个任务中实现了竞争力甚至更优的表现，并在重复优化中展现出更强的稳定性。因此，我们建议将tidyHEBO作为实际序列实验的可靠工具，以及未来贝叶斯优化研究的通用基准。
原文摘要 · Abstract (English)
Bayesian optimization is increasingly used to guide data-efficient experimentation in chemistry, materials science, and related laboratory settings, but its practical performance depends strongly on how well surrogate-model assumptions match the geometry and noise structure of the underlying objective. We introduce tidyHEBO, a robust Bayesian optimization model inspired by heteroskedastic evolutionary Bayesian optimization (HEBO) for single-objective, sequential optimization. tidyHEBO reconstructs the HEBO design philosophy in BoTorch and revises surrogate training, output-warping selection, acquisition function evaluation, and Pareto-front search. We benchmarked tidyHEBO on synthetic functions, Olympus emulators, fully experimental reaction-optimization datasets, needle-in-a-haystack (NIAH) materials problems, and Bayesmark hyperparameter optimization tasks. On these tasks tidyHEBO achieved competitive to superior performance and improvement in robustness across repeated optimization runs. We therefore propose tidyHEBO as a practical tool for sequential experimentations and a strong general-purpose benchmark for future Bayesian optimization research.
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