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.

基于高斯过程 surrogate 模型，利用 Hamiltonian Monte Carlo 进行推断，能够迅速识别与建模未知目标函数相关的空间稀疏子空间，实现高维贝叶斯优化 (Bayesian optimization) 中样本效率与性能的权衡。