Sampling-Based Batch Sequential Design by Stein Variational Gradient Descent
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.
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BibTeX
@misc{fu2026samplingbased,
title = {{Sampling-Based Batch Sequential Design by Stein Variational Gradient Descent}},
author = {Penghui Fu and Xiaoxian Ding and Chunlin Ji and Jianhua Z. Huang and C. F. Jeff Wu},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.20583},
eprint = {2609.20583},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.20583},
}
RIS
TY - UNPB
TI - Sampling-Based Batch Sequential Design by Stein Variational Gradient Descent
AU - Penghui Fu
AU - Xiaoxian Ding
AU - Chunlin Ji
AU - Jianhua Z. Huang
AU - C. F. Jeff Wu
PY - 2026
DA - 2026/09/18
PB - arXiv
DO - 10.48550/arxiv.2609.20583
UR - https://arxiv.org/abs/2609.20583
AB - 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.
ER -