近期研究论文探讨了用于复杂问题的贝叶斯优化（BO）技术的进展。一项研究介绍了“脱离循环”（Out-Of-The-Loop）MF-BO，它结合了历史高保真数据，在最高保真度直接访问成本过高时改进优化。另一篇论文提出了一种双层BO方法，该方法利用具有黑盒和白盒变量的问题中的可分离性，在基准测试中表现优于标准方法。此外，还提出了一个名为“Tempered Posteriors”的框架，通过调整高斯过程代理来增强BO的鲁棒性，在局部采样下表现出改进的性能。最后，用于超参数优化中BO的动态先验框架允许持续的用户影响，在与竞争对手的比较中持续表现优异。
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arXiv:2608.00641v2 Announce Type: replace Abstract: Bayesian optimization (BO) relies on a surrogate model and an acquisition function, yet the most suitable choices vary across tasks and optimization stages. Automated Bayesian optimization (AutoBO) addresses this variability by …
arXiv:2608.04113v1 Announce Type: cross Abstract: Black-box optimization is a ubiquitous problem in science and engineering, often dealing with expensive objective functions with cheaper lower-fidelity proxies available. Multi-fidelity Bayesian optimization (MF-BO) is a principle…
arXiv cs.LG
TIER_1English(EN)·Joshua E. Hammond, Tyler A. Soderstrom, Brian A. Korgel, Michael Baldea·
arXiv:2608.03045v1 Announce Type: new Abstract: We consider grey-box optimization problems where the decision variables naturally partition into black-box variables (as arguments to an expensive black-box function) and white-box variables, governed by a set of explicit, closed-fo…
arXiv:2601.07094v2 Announce Type: replace-cross Abstract: Bayesian optimization (BO) iteratively fits a Gaussian process (GP) surrogate to accumulated evaluations and selects new queries via an acquisition function. Under local misspecification, this feedback loop can produce ove…
arXiv cs.LG
TIER_1English(EN)·Lukas Fehring, Marcel Wever, Maximilian Splieth\"over, Leona Hennig, Henning Wachsmuth, Marius Lindauer·
arXiv:2511.02570v3 Announce Type: replace Abstract: Bayesian optimization (BO) is a widely used approach to hyperparameter optimization (HPO). However, most existing HPO methods only incorporate expert knowledge during initialization, limiting practitioners' ability to influence …
