Publications and Preprints

Publications

  • Orthogonalized kernel debiased machine learning for multimodal data analysis. [pdf]

Xiaowu Dai and Lexin Li.

Journal of the American Statistical Association: Theory and Methods (JASA), 2021.

  • Kernel knockoffs selection for nonparametric additive models. [pdf]

Xiaowu Dai, Xiang Lyu, and Lexin Li.

Journal of the American Statistical Association: Theory and Methods (JASA), 2021.

  • Learning strategies in decentralized matching markets under uncertain preferences. [pdf]

Xiaowu Dai and Michael Jordan.

Journal of Machine Learning Research (JMLR), 2021.

  • Learning in multi-stage decentralized matching markets. [online][preprint]

Xiaowu Dai and Michael Jordan.

Advances in Neural Information Processing Systems (NeurIPS), 2021.

Xiaowu Dai and Lexin Li.

Journal of the American Statistical Association: Theory and Methods (JASA), 2021.

  • A synthesis of pathways linking diet, metabolic risk and cardiovascular disease: A framework to guide further research and approaches to evidence-based practice. [journal]

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), 2021.

  • On large batch training and sharp minima: A Fokker-Planck perspective. [journal]

Xiaowu Dai and Yuhua Zhu.

Journal of Statistical Theory and Practice (JSTP), special issue on "Advances in Deep Learning", 2020. 

  • High-dimensional smoothing splines and application in Alzheimer's disease prediction using magnetic resonance imaging. [journal][preprint]

Xiaowu Dai.

Statistics in Biopharmaceutical Research (SBR), 2019.

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

Xiaowu Dai. 

Alzheimer's & Dementia (AD), 2018.

Preprints

  • Multi-layer kernels: Multi-scale nonparametric regression and confidence bands. [pdf]

Xiaowu Dai.

Under Revisions at Annals of Statistics (AoS), 2021+.

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

Xiaowu Dai and Peter Chien. 

Under Revisions at Journal of the American Statistical Association (JASA), 2021+.

[Student Research Grants Competition Award, UW-Madison, 2019]

[ICSA Student Paper Award, 2018]

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

Xiaowu Dai and Lexin Li. 

Preprint, 2021+.

  • 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. [pdf]

Xiaowu Dai, Saad Mouti, Marjorie Lima do Vale, Sumantra Ray, Jeffrey Bohn, and Lisa Goldberg. 

Preprint, 2021+.

  • Towards theoretical understanding of large batch training in stochastic gradient descent. [pdf]

Xiaowu Dai and Yuhua Zhu. 

Preprint, 2021+.

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

Xiaowu Dai and Jared Huling.

Preprint, 2021+.

  • Minimax optimal rates of estimation in functional ANOVA models with derivatives. [pdf]

Xiaowu Dai and Peter Chien.

Preprint, 2021+.

[ASA Nonparametric Statistics Section Student Paper Award, 2018]

[IMS Hannan Graduate Student Travel Award, 2018]

Miscellaneous

  • Statistical learning-aided design for a blockchain payment system. [pdf]

Xiaowu Dai. 

Research Vignette, Simons Institute for the Theory of Computing, 2020.

  • Statistical machine learning for complex data sets. [pdf]

Xiaowu Dai. 

Ph.D. Thesis, Department of Statistics, UW-Madison, 2019.