AI 中文总结
针对实际异方差和非平稳问题，引入受HEBO启发的tidyHEBO模型，重构其设计理念并修订多方面内容。经多任务基准测试，该模型性能具竞争力且鲁棒性强，可作顺序实验实用工具和贝叶斯优化研究通用基准。
AI 中文摘要
贝叶斯优化越来越多地用于指导化学、材料科学及相关实验室环境中的数据高效实验，但其实际性能很大程度上取决于代理模型假设与潜在目标的几何结构和噪声结构的匹配程度。我们引入了tidyHEBO，这是一种受异方差进化贝叶斯优化（HEBO）启发的用于单目标、顺序优化的强大贝叶斯优化模型。tidyHEBO在BoTorch中重构了HEBO设计理念，并修订了代理训练、输出扭曲选择、采集函数评估和帕累托前沿搜索。我们在合成函数、奥林巴斯模拟器、完全实验反应优化数据集、大海捞针（NIAH）材料问题和Bayesmark超参数优化任务上对tidyHEBO进行了基准测试。在这些任务中，tidyHEBO取得了具有竞争力甚至更优的性能，并且在重复优化运行中提高了鲁棒性。因此，我们提出tidyHEBO作为顺序实验的实用工具和未来贝叶斯优化研究的强大通用基准。
英文摘要
Bayesian optimization (BO) is widely used for expensive black-box problems, yet practical performance depends not only on high-level algorithmic choices but also on how surrogate model training, input and output warping transformations, acquisition functions, and candidate search are implemented. We present tidyHEBO, a BoTorch-native single-objective optimizer designed for robust general-purpose optimization. tidyHEBO jointly fits Yeo-Johnson output warping with the Gaussian-process surrogate, evaluates acquisition functions on the original objective scale using deterministic quadrature or MC-samples, and performs constrained cumulative Pareto search over multiple acquisition criteria. Without any Olympus-specific hyperparameter tuning - using only default optimizer configurations - tidyHEBO ranked first among the evaluated methods on the Olympus benchmark. It achieved the best average ranks for typical performance (average rank 1.53), worst-tail performance (1.21), and run-to-run variability (2.00), measured by median nAUC, CVaR_nAUC, and IQR_nAUC, respectively. Using the same default configuration, tidyHEBO also performed strongly on synthetic and Needle-in-a-Haystack problems and closely matched HEBO on Bayesmark (92.64 versus 93.34) while exceeding GP with logarithmic expected improvement and random search. Adaptive batching reduced feedback rounds while revealing a controllable trade-off between parallelization and optimization quality as the batch cap increased. These results characterize tidyHEBO as a robust, reproducible general-purpose optimizer for a broad range of practical optimization problems, including scientific applications and hyperparameter tuning.
