论文标题
蒙特卡洛树搜索基于高维贝叶斯优化的变量选择
Monte Carlo Tree Search based Variable Selection for High Dimensional Bayesian Optimization
论文作者
论文摘要
贝叶斯优化（BO）是一类流行方法，用于昂贵的黑盒优化，并已广泛应用于许多情况。但是，BO遭受了维度的诅咒，将其扩展到高维问题仍然是一个挑战。在本文中，我们提出了一种基于蒙特卡洛树搜索（MCT）的变量选择方法MCTS-VS，以迭代选择和优化变量的子集。也就是说，MCTS-VS通过MCT构建低维子空间，并使用任何BO算法在子空间中进行优化。我们对通用变量选择方法进行理论分析，以揭示其如何工作。对高维合成功能和现实世界中的问题（即NAS基础问题和Mujoco运动任务）的实验表明，配备适当BO Optimizer的MCTS-VS可以实现最新的性能。
Bayesian optimization (BO) is a class of popular methods for expensive black-box optimization, and has been widely applied to many scenarios. However, BO suffers from the curse of dimensionality, and scaling it to high-dimensional problems is still a challenge. In this paper, we propose a variable selection method MCTS-VS based on Monte Carlo tree search (MCTS), to iteratively select and optimize a subset of variables. That is, MCTS-VS constructs a low-dimensional subspace via MCTS and optimizes in the subspace with any BO algorithm. We give a theoretical analysis of the general variable selection method to reveal how it can work. Experiments on high-dimensional synthetic functions and real-world problems (i.e., NAS-bench problems and MuJoCo locomotion tasks) show that MCTS-VS equipped with a proper BO optimizer can achieve state-of-the-art performance.
