论文标题
重新检查高维贝叶斯优化的线性嵌入
Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization
论文作者
论文摘要
贝叶斯优化（BO）是一种优化昂贵评估的黑盒功能的流行方法。 BO中的一个重大挑战是在保持样本效率的同时，扩展到高维参数空间。现有文献中考虑的解决方案通常是通过随机线性嵌入将高维空间嵌入较低维的歧管中。在本文中，我们确定了有关BO使用线性嵌入的几个关键问题和误解。我们研究了文献中线性嵌入的特性，并表明当前方法中的某些设计选择会对其性能产生不利影响。我们从经验上表明，正确解决这些问题可以显着提高线性嵌入对BO在一系列问题上的功效，包括学习机器人运动的步态政策。
Bayesian optimization (BO) is a popular approach to optimize expensive-to-evaluate black-box functions. A significant challenge in BO is to scale to high-dimensional parameter spaces while retaining sample efficiency. A solution considered in existing literature is to embed the high-dimensional space in a lower-dimensional manifold, often via a random linear embedding. In this paper, we identify several crucial issues and misconceptions about the use of linear embeddings for BO. We study the properties of linear embeddings from the literature and show that some of the design choices in current approaches adversely impact their performance. We show empirically that properly addressing these issues significantly improves the efficacy of linear embeddings for BO on a range of problems, including learning a gait policy for robot locomotion.
