Publications​

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

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2024

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• Nonparametric estimation via partial derivatives. [journal][preprint][UCLA StatsDS News][UCLA Physical Sciences News]

Xiaowu Dai*.

Journal of the Royal Statistical Society: Series B (JRSSB), 2024. [lead article]​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​

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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*.

Preprint, 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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• Conformal online auction design. [pdf][code][older version, code]

Jiale Han and Xiaowu Dai*.   

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. [lead article]

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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 (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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• Selection and estimation optimality in high dimensions with the TWIN penalty. [pdf]

Xiaowu Dai and Jared Huling.​ 

Preprint, 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. [lead article]

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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. [lead article]

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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. [lead article]

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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.