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Uncertainty Quantification with ML Systems
[Multimodal Inference, Distribution-Free Inference, Kernel-Based Learning]
(Note: students under my supervision; † equal contribution; * corresponding author)​​
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Multimodal Inference
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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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Surrogate-powered inference: Regularization and adaptivity.
Jianmin Chen, Huiyuan Wang, Thomas Lumley, Xiaowu Dai, and Yong Chen.
Preprint, 2025.
Distribution-Free Inference
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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 Major Revisions at Journal of the American Statistical Association (JASA), 2025.
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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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Kernel-Based Learning​
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Xiaowu Dai*, Xiang Lyu, and Lexin Li.
Journal of the American Statistical Association: Theory and Methods (JASA), 2023.
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Xiaowu Dai*.
Journal of the Royal Statistical Society Series B: Statistical Methodology (JRSSB), 2024.
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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.
Under R&R at Journal of the American Statistical Association (JASA), 2025.
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Another look at statistical calibration: A non-asymptotic theory and prediction-oriented optimality. [pdf]
Xiaowu Dai and Peter Chien.
Preprint, 2023.
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Jiale Han, Xiaowu Dai*, and Yuhua Zhu.
Preprint, 2025.