发表机构
King’s College London（伦敦国王学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对可扩展多保真贝叶斯优化，在相同条件下对11种迁移学习代理和4种GP方法进行基准测试，发现迁移学习代理在分子和材料问题上表现更佳，贪婪利用迁移学习均值是更稳健选择，是分子和材料MFBO的首选代理。
AI 中文摘要
自动驾驶实验室越来越依赖多保真贝叶斯优化（MFBO）来平衡廉价、近似评估与稀缺、昂贵评估，其核心是预测代理。高斯过程（GPs）是默认选择，但随着数据积累扩展性差且假设的平滑态势常被分子和材料搜索空间违反。迁移学习提供了适合此情况的替代方案，能从大量廉价数据学习表示并适应稀疏昂贵数据。本文在相同选择规则、保真度预算和模型大小下，对11种迁移学习代理和4种GP方法在9个任务上进行基准测试。结果表明，GPs在平滑、低维函数上表现好，在分子和材料问题上最差，迁移学习代理用更少计算能得到更好解决方案。由于代理的获取策略固定，优势归因于代理本身。不确定性驱动探索并非可靠有益，校准也不能预测优化性能，所以贪婪利用迁移学习均值是更稳健的默认选择。因此，迁移学习是分子和材料MFBO的首选代理。
英文摘要
Self-driving laboratories increasingly rely on multi-fidelity Bayesian optimization (MFBO) to balance cheap, approximate evaluations against scarce, expensive ones, with a predictive surrogate at its core. Gaussian processes (GPs) are the default choice, but they scale poorly as data accumulate and assume a smooth landscape that molecular and materials search spaces routinely violate. Transfer learning offers an alternative suited to this regime: it learns a representation from abundant cheap data and adapts it to sparse expensive data. Despite its use in property prediction, transfer learning has not been tested as the engine of a closed-loop optimization. Here we benchmark eleven transfer-learning surrogates against four GP methods under an identical selection rule, fidelity budget, and model size, across nine tasks spanning synthetic functions to real chemistry and materials problems. GPs win on smooth, low-dimensional functions but perform worst on molecular and materials problems, where transfer-learning surrogates reach substantially better solutions using far less computation. Because acquisition policy is held fixed across surrogates, this advantage is attributable to the surrogate itself. Uncertainty-driven exploration is not reliably beneficial, and calibration does not predict optimization performance, so greedy exploitation of the transfer-learned mean is the more robust default. Transfer learning is therefore the surrogate of choice for molecular and materials MFBO.
