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

Jiale Han, Xiaowu Dai*, and Yuhua Zhu.

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.