Bayesian optimization (BO) is a powerful paradigm for efficient optimization of black-box objective functions. High-dimensional BO presents a particular challenge, in part because the curse of dimensionality makes it difficult to define -- as well as do inference over -- a suitable class of surrogate models. We argue that Gaussian process surrogate models defined on sparse axis-aligned subspaces offer an attractive compromise between flexibility and parsimony. We demonstrate that our approach, which relies on Hamiltonian Monte Carlo for inference, can rapidly identify sparse subspaces relevant to modeling the unknown objective function, enabling sample-efficient high-dimensional BO. In an extensive suite of experiments comparing to existing methods for high-dimensional BO we demonstrate that our algorithm, Sparse Axis-Aligned Subspace BO (SAASBO), achieves excellent performance on several synthetic and real-world problems without the need to set problem-specific hyperparameters.

翻译：贝叶斯优化(BO)是高效优化黑箱目标功能的强大范例。 高维BO是一个特殊的挑战,部分原因是由于对维度的诅咒使得难以界定 -- -- 以及推断 -- -- 合适的代用模型类别。 我们争论说,在低轴轴对齐的子空间上定义的戈西亚进程代用模型在灵活性和面孔之间提供了有吸引力的折中。我们证明,我们依靠汉密尔顿·蒙特卡洛的推理方法,可以迅速识别与建立未知目标功能模型有关的稀疏亚空间,使样本效率高维度BO得以建立。在与现有高维BO方法相比的广泛一系列实验中,我们证明我们的算法,Sparse Axis-Axis-Asragable Subspspace BO(SAASBO),在几个合成和现实世界问题上取得了出色表现,而无需设定特定问题的超参数。