High-Dimensional Inference

Learning and Uncertainty Quantification

[Kernel methods, Uncertainty quantification, Causal machine learning]

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

Causal machine learning

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.

Uncertainty quantification

Discussion: "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.

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.

Kernel methods

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.

Xiaowu Dai*.

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

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

Xiaowu Dai and Peter Chien.

Preprint, 2023.

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

Xiaowu Dai* and Huiying Zhong.

Preprint, 2024.

Jiale Han, Xiaowu Dai*, and Yuhua Zhu.

Preprint, 2025.

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

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

Preprint, 2025.