ICML 2026poster0 citations
Robust Sequential Experimental Design for A/B Testing
Qianglin Wen, Xiangkun Wu, Chengchun Shi, Ting Li, Niansheng Tang, Yingying Zhang, Hongtu Zhu
Abstract
Experimental design has emerged as a powerful approach for improving the sample efficiency of A/B testing, yet existing designs rely critically on correctly specified models. We study robust sequential experimental design under model misspecification and develop a unified framework that covers both contextual bandit and dynamic settings. Theoretically, we prove that our design bounds the worst-case mean squared error of the estimated treatment effect. Empirically, we demonstrate the effectiveness of the proposed approach using synthetic and real-world datasets from a leading technology company.
TheoryRobustnessBenchmark
BibTeX
@inproceedings{
wen2026robust,
title={Robust Sequential Experimental Design for A/B Testing},
author={Qianglin Wen and Xiangkun Wu and Chengchun Shi and Ting Li and Niansheng Tang and Yingying Zhang and Hongtu Zhu},
booktitle={Forty-third International Conference on Machine Learning},
year={2026},
url={https://openreview.net/forum?id=Q7lEZjtDKO}
}