Publications​

(Note:     students under my supervision;  * corresponding author)​​

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2025

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• Online auction design using distribution-free uncertainty quantification with applications to e-commerce. [pdf][code][older version, code]

Jiale Han and Xiaowu Dai*.   

Under R&R at Journal of the American Statistical Association (JASA), 2025.

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• Learn then decide: A learning approach for designing data marketplaces. [pdf][code]

Yingqi Gao, Jin Zhou, Hua Zhou, Yong Chen, and Xiaowu Dai*.

Preprint, 2025.

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• Variance reduction via resampling and experience replay. [pdf][code]

Jiale Han, Xiaowu Dai*, and Yuhua Zhu*.

Preprint, 2025.​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​

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• Quantifying microbial interactions based on compositional data using an iterative approach for solving Lotka-Volterra equations. 

Yue Huang, Tianqi Tang, Xiaowu Dai, and Fengzhu Sun.

Preprint, 2025.

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• Effect decomposition of functional-output computer experiments via orthogonal additive Gaussian processes.

Yu Tan, Yongxiang Li, Xiaowu Dai, and Kwok-Leung Tsui.

Preprint, 2025.

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2024

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• Nonparametric estimation via partial derivatives. [journal][preprint]

Xiaowu Dai*.

Journal of the Royal Statistical Society Series B: Statistical Methodology (JRSSB), 2024.​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​

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• Post-regularization confidence bands for ordinary differential equations. [journal][preprint]

Xiaowu Da​i* and Lexin Li.

Journal of Machine Learning Research (JMLR), 2024.

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• Incentive-aware recommender systems in two-sided markets. [journal][reprint][preprint][code]

Xiaowu Dai*, Wenlu Xu, Yuan Qi, and Michael Jordan.

ACM Transactions on Recommender Systems (TORS), 2024.

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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 data envelopment analysis approach for assessing fairness in resource allocation: Application to kidney exchange programs. [pdf][code]

Ali Kaazempur-Mofrad, and Xiaowu Dai*.

Under R&R at Annals of Applied Statistics (AoAS), 2024.

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• Fairness-aware organ exchange and kidney paired donation. [pdf][code]

Mingrui Zhang, Xiaowu Dai*, and Lexin Li*.

Preprint, 2024.

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• Dynamic online recommendation for two-sided market with Bayesian incentive compatibility. [pdf]

Yuantong Li, Guang Cheng, and Xiaowu Dai*.

Preprint, 2024.

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• Multi-layer kernel machines: Fast and optimal nonparametric regression with uncertainty quantification. [pdf][code][PyPI]

Xiaowu Dai* and Huiying Zhong.  

Preprint, 2024.

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2023

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• Kernel knockoffs selection for nonparametric additive models. [journal][reprint][preprint][code]

Xiaowu Dai*, Xiang Lyu, and Lexin Li.

Journal of the American Statistical Association: Theory and Methods (JASA), 2023.

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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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• 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.

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• An ODE model for dynamic matching in heterogeneous networks. [pdf][code]

Xiaowu Dai* and Hengzhi He.

Preprint, 2023.

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2022​

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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: Theory and Methods (JASA), 2022. 

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• Kernel ordinary differential equations. [journal][reprint][preprint]

Xiaowu Dai and Lexin Li.​

Journal of the American Statistical Association: Theory and Methods (JASA), 2022. 

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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), 2022.

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• Another look at statistical calibration: A non-asymptotic theory and prediction-oriented optimality. [pdf]

Xiaowu Dai and Peter Chien.​ 

Preprint, 2022.

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pre-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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• 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.

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• High-dimensional varying coefficient models for Alzheimer's disease diagnosis with longitudinal and heterogeneous structural MR images.

Xiaowu Dai.

Alzheimer's & Dementia (AD), 2018.