发表机构
The Chinese University of Hong Kong, Shenzhen; Kuang-Chi Institute of Advanced Technology（香港中文大学（深圳）; 旷视先进技术研究）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于斯坦变分梯度下降的采样框架，将完全序贯实验设计转换为批量序贯方法，平衡个体效用与批量多样性，并通过数值研究验证性能。
AI 中文摘要
许多现实世界中的实验设计问题需要在多个阶段进行批量实验运行，其中每个阶段会选择并评估多个点。然而，设计文献中的大多数工作都专注于完全序贯（逐点）方法。本文提出了一种基于采样的框架，系统地将完全序贯方法转换为批量序贯方法。具体而言，我们采用斯坦变分梯度下降（SVGD）来从适当构造的目标分布中高效地采样一批点，同时平衡个体效用和批量多样性。我们解决了将SVGD用于实验设计时出现的挑战，包括受限设计区域和近似均匀的目标分布。我们将所提出的方法应用于获得最先进的完全序贯方法的批量版本，并通过广泛的数值研究展示了其性能。
英文摘要
Many real-world experimental design problems require a batch of experimental runs across stages, in which multiple points are selected and evaluated at each stage. However, most work in the design literature is focused on fully sequential (point-by-point) methods. This paper proposes a sampling-based framework to systematically convert a fully sequential method to a batch sequential method. In particular, Stein variational gradient descent (SVGD) is adapted to efficiently sample a batch of points from a properly constructed target distribution while balancing the individual utility and the batch diversity. We address challenges that arise in using SVGD for experimental designs, including constrained design regions and near-uniform target distributions. We apply the proposed method to obtain batch versions of the state-of-the-art fully sequential methods, and demonstrate their performance through extensive numerical studies.
