发表机构
Nagoya Institute of Technology（名古屋工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出利用高斯过程样本路径估计累积遗憾，并通过外部贝叶斯优化自动选择内部BO算法的参数配置，以提升黑盒优化性能。
AI 中文摘要
贝叶斯优化（BO）被广泛用作昂贵的黑盒优化的标准方法。然而，BO算法通常涉及必须预先指定的参数，且其性能可能强烈依赖于这些选择。我们提出一个框架，利用从BO开始时可用信息推断出的高斯过程（GP）所生成的样本路径来优化此类参数。我们使用累积遗憾作为BO算法的性能指标。通过在生成的样本路径上运行BO算法，我们获得给定参数配置下其期望累积遗憾的经验估计。优化该估计使我们能够识别在给定当前可用信息下预期能实现低累积遗憾的参数配置。由于该参数优化本身是一个黑盒优化问题，我们采用另一个BO过程来解决它，称之为外部BO。通过实验，我们证明了所提出的框架能够有效地选择在众多候选配置中实现强性能的参数配置。
英文摘要
Bayesian optimization (BO) is widely used as a standard approach for expensive black-box optimization. However, BO algorithms often involve parameters that must be specified in advance, and their performance can strongly depend on these choices. We propose a framework for optimizing such parameters using sample paths drawn from a Gaussian process (GP) inferred from the information available at the start of BO. We use cumulative regret as the performance metric for a BO algorithm. By running the BO algorithm on the generated sample paths, we obtain an empirical estimate of its expected cumulative regret for a given parameter configuration. Optimizing this estimate allows us to identify parameter configurations that, given the currently available information, are expected to achieve low cumulative regret. Since this parameter optimization is itself a black-box optimization problem, we employ another BO procedure to solve it, which we refer to as outer BO. Through experiments, we demonstrate that the proposed framework can effectively select parameter configurations that achieve strong performance among a range of candidate configurations.
