Sampling-Based Batch Sequential Design by Stein Variational Gradient Descent

2026-09-17

AI Summary

本文提出了一种基于Stein变分梯度下降的抽样框架，将完全顺序设计方法转换为批量顺序设计方法，以解决实验设计中的批量选择问题。

Abstract

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.

Problem

• batch sequential design
• fully sequential methods
• Stein variational gradient descent
• experimental design

Innovation

• Sampling-based Framework
• Batch Sequential Design
• Stein Variational Gradient Descent (SVGD)
• Target Distribution