Maximizing high-dimensional, non-convex functions through noisy observations
is a notoriously hard problem, but one that arises in many applications. In
this paper, we tackle this challenge by modeling the unknown function as a
sample from a high-dimensional gaussian process (GP) distr
对高维优化问题进行系统研究发现标准高斯过程贝叶斯优化(BO)在很多合成和真实世界基准问题中表现出色,在高维优化上经常比专门设计的现有 BO 方法更出色,同时具备适应各种目标函数结构的鲁棒性,单纯使用最大似然估计即可获得有前景的优化性能,不需要复杂的马尔可夫链蒙特卡洛采样,因此建议重新评估和深入研究标准 BO 在解决高维问题方面的潜力。