Selected Publications
Listed in reverse chronological order by date of original manuscript. Later publication citations added where applicable.
(Note: students under my supervision; † equal contribution; * corresponding author)
2026
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A statistical market-design framework for academic job markets. [pdf][code][project website]
Ali Kaazempur-Mofrad, Xiaowu Dai*, and Xuming He.
Preprint, 2026.
Yang Sui, Jin Zhou, Hua Zhou, and Xiaowu Dai*.
Preprint, 2026.
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Statistical matching markets under uncertain preferences. [pdf][code]
Yiran Huang, Hanwen Ye, Xiaowu Dai*, and Annie Qu*.
Preprint, 2026.
Angel Rodrigo Avelar Menendez, Yufeng Liu, and Xiaowu Dai*.
Preprint, 2026.
Baiting Chen, Tong Zhu, Xuanang Li, Yichi Zhang, and Xiaowu Dai*.
Preprint, 2026.
Mathew Chandy, Michael Johnson, Judong Shen, Devan V. Mehrotra, Hua Zhou, Jin Zhou, and Xiaowu Dai*.
Preprint, 2026.
Tong Zhu†, Baiting Chen†, Jin Zhou, Hua Zhou, Sriram Sankararaman, and Xiaowu Dai*.
Preprint, 2026.
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Mechanism design for quality-preserving LLM advertising. [pdf][code]
Jiale Han, and Xiaowu Dai*.
Preprint, 2026.
2025
Yingqi Gao, Wenlu Xu, Jin Zhou, Hua Zhou, Yong Chen, and Xiaowu Dai*.
Journal of the American Statistical Association (JASA), 2026.
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Surrogate-powered inference: Regularization and adaptivity. [pdf]
Jianmin Chen, Huiyuan Wang, Thomas Lumley, Xiaowu Dai, and Yong Chen.
Under Major Revisions at Journal of the American Statistical Association (JASA), 2025.
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Effect decomposition of functional-output computer experiments via orthogonal additive Gaussian processes. [pdf]
Yu Tan, Yongxiang Li, Xiaowu Dai, and Kwok-Leung Tsui.
Under Major Revisions at Journal of the American Statistical Association (JASA), 2025.
Baiting Chen†, Tong Zhu†, Jiale Han, Lexin Li, Gang Li, and Xiaowu Dai*.
Advances in Neural Information Processing Systems (NeurIPS), 2025.
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Variance reduction via resampling and experience replay. [proceedings][reprint][preprint][code]
Jiale Han, Xiaowu Dai*, and Yuhua Zhu*.
Fortieth AAAI Conference on Artificial Intelligence (AAAI), 2026. Selected for oral presentation.
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Auto-bidding under return-on-spend constraints with uncertainty quantification. [proceedings][reprint][preprint][code]
Jiale Han, Chun Gan, Chengcheng Zhang, Jie He, Zhangang Lin, Ching Law, and Xiaowu Dai*.
The ACM Web Conference (WWW), 2026. Selected for oral presentation.
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Quantifying microbial interactions based on compositional data using an iterative approach for solving generalized Lotka-Volterra equations. [journal][reprint][code]
Yue Huang, Tianqi Tang, Xiaowu Dai, and Fengzhu Sun.
PLoS Computational Biology (PCBI), 2025.
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On MM algorithms for decentralized data. [pdf][code]
Yudong Wang, Jie Hu, Xiaowu Dai, Raymond Carroll, and Yong Chen.
Preprint, 2025
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Direct and indirect persuasion effects under directional interference. [pdf][code]
Wenlu Xu, Xiaowu Dai*, and Lin Liu.
Preprint, 2025.
2022 and earlier
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Orthogonalized kernel debiased machine learning for multimodal data analysis. [journal][reprint][preprint]
Xiaowu Dai and Lexin Li.
Journal of the American Statistical Association (JASA), 2022.
Xiaowu Dai and Lexin Li.
Journal of the American Statistical Association (JASA), 2022.
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Learning strategies in decentralized matching markets under uncertain preferences. [journal][preprint]
Xiaowu Dai and Michael Jordan.
Journal of Machine Learning Research (JMLR), 2021.
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Learning in multi-stage decentralized matching markets. [proceedings][preprint]
Xiaowu Dai and Michael Jordan.
Advances in Neural Information Processing Systems (NeurIPS), 2021.
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A synthesis of pathways linking diet, metabolic risk and cardiovascular disease: A framework to guide further research and approaches to evidence-based practice. [journal][reprint][pubmed]
Marjorie Lima do Vale, Luke Buckner, Claudia-Gabriela Mitrofan, Kai Sento Kargbo, Rajna Golubic, Ali Ahsan Khalid, Sammyia Ashraf, Saad Mouti, Xiaowu Dai, David Unwin, Jeffrey Bohn, Lisa Goldberg, and Sumantra Ray.
Nutrition Research Reviews (NRR), 2021.
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On large batch training and sharp minima: A Fokker-Planck perspective. [journal][reprint][preprint][older version]
Xiaowu Dai and Yuhua Zhu.
Journal of Statistical Theory and Practice (JSTP), special issue on "Advances in Deep Learning", 2020.
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High-dimensional smoothing splines and application in Alzheimer's disease prediction using magnetic resonance imaging. [journal][reprint][preprint]
Xiaowu Dai.
Statistics in Biopharmaceutical Research (SBR), 2020.
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Statistical learning-aided design for a blockchain payment system. [pdf]
Xiaowu Dai.
Research Vignette, Simons Institute for the Theory of Computing, 2020.
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Statistical machine learning for complex data sets. [pdf]
Xiaowu Dai.
Ph.D. Thesis, Department of Statistics, UW-Madison, 2019.
2024
Xiaowu Dai*.
Journal of the Royal Statistical Society Series B: Statistical Methodology (JRSSB), 2025.
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Online auction design using distribution-free uncertainty quantification with applications to e-commerce. [journal][reprint][preprint][code]
Jiale Han and Xiaowu Dai*.
Journal of the American Statistical Association (JASA), 2026.
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A data envelopment analysis approach for assessing fairness in resource allocation: Application to kidney exchange programs. [journal][reprint][preprint][code]
Ali Kaazempur-Mofrad, and Xiaowu Dai*.
Annals of Applied Statistics (AoAS), 2025.
Xiaowu Dai*, Wenlu Xu, Yuan Qi, and Michael Jordan.
ACM Transactions on Recommender Systems (ToRS), 2024.
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Incentivized exploration with stochastic covariates: A two-stage mechanism design for recommender system. [pdf]
Yuantong Li, Guang Cheng, and Xiaowu Dai*.
International Conference on Machine Learning (ICML), 2026.
Mingrui Zhang, Xiaowu Dai*, and Lexin Li*.
Statistics in Biosciences (SIBS), 2026.
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Multi-layer kernel machines: Fast and optimal nonparametric regression with uncertainty quantification. [pdf][code]
Wenlu Xu†, Tong Zhu†, Huiying Zhong, and Xiaowu Dai*.
Under R&R at Journal of Machine Learning Research (JMLR), 2024.
2023
Xiaowu Dai*, Xiang Lyu, and Lexin Li.
Journal of the American Statistical Association (JASA), 2023.
Xiaowu Dai* and Lexin Li.
Journal of Machine Learning Research (JMLR), 2024.
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Discussion of "Estimating means of bounded random variables by betting" by Waudby-Smith and Ramdas. [journal][reprint][preprint][code]
Jiayi Li, Yuantong Li, and Xiaowu Dai*.
Journal of the Royal Statistical Society Series B: Statistical Methodology (JRSSB), 2023.
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Two-sided competing matching recommendation markets with quota and complementary preferences constraints. [proceedings][preprint][code]
Yuantong Li, Guang Cheng, and Xiaowu Dai*.
International Conference on Machine Learning (ICML), 2024.
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A robust multi-item auction design with statistical learning. [proceedings][preprint][code]
Jiale Han and Xiaowu Dai*.
ICLR Workshop on AI for Mechanism Design and Strategic Decision Making, 2026.
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A resampling approach for causal inference on novel two-point time-series with application to identify risk factors for type-2 diabetes and cardiovascular disease. [journal][reprint][preprint][code]
Xiaowu Dai*, Saad Mouti, Marjorie Lima do Vale, Sumantra Ray, Jeffrey Bohn, and Lisa Goldberg.
Statistics in Biosciences (SIBS), 2023.