(# indicates authors contributed equally or authors are listed in alphabetical order; @ indicates students mentored by me )
AI & Machine Learning:
@ Zhou, G., Han, Y., and Yu, X. (just accepted).
"Covariate-adjusted deep causal learning for heterogeneous panel data models. " - Preprint
[ Winner of 2026 ICSA Applied Statistics Symposium Student Paper Award, awarded to Guanhao Zhou ].
NeurIPS 2026. [Acceptance rate: 25.7%]
Li, S., Han, Y., and Yu, X. (just accepted).
"Factor augmented high-dimensional stochastic gradient descent. " - Preprint
NeurIPS 2026. [Acceptance rate: 25.7%]
@ Gu, Y. and Yu, X. (2026).
"Power-boosted Granger-causal discovery for large heterogeneous panel data" - link
ICML 2026. [Acceptance rate: 26.6%]
@ Luo, Z., Han, Y., and Yu, X. (2026).
"Supervised dynamic dimension reduction with deep neural network" - link
AAAI 2026. [Acceptance rate: 17.6%]
@ Zhou, G., Han, Y., and Yu, X. (2025).
"Factor Augmented Tensor-on-Tensor Neural Networks" - link
AAAI 2025. [Acceptance rate: 23.4%]
Statistical Methods & Theory:
Yu, X. and Kelley, K. (in press).
"Power enhancement in high-dimensional heterogeneous mediation analysis" - link
Journal of the American Statistical Association.
@ Zhou, G., Han, Y., and Yu, X. (in press).
"Discussion of 'Matrix Completion When Missing Is Not at Random and Its Applications in Causal Panel Data Models' "
Journal of the American Statistical Association.
Zhang, Z., Yu, X., and Li, R. (2025).
"A novel approach of high dimensional linear hypothesis testing problem" - link, pdf
Journal of the American Statistical Association.
Yu, X., Zhang, L., Srinivasan, A., Xie, M.-G., and Xue, L. (2025).
"A unified combination framework for dependent tests with applications to microbiome association studies" - link, pdf, code
Biometrics.
# Li, D., # Xue, L., # Yang, H., and # Yu, X. (2025).
"Power-enhanced two-sample mean tests for high-dimensional microbiome compositional data" - link, pdf
Biometrics.
Yu, X., Li, D., and Xue, L. (2024).
"Fisher's combined probability test for high-dimensional covariance matrices" - link, pdf, code
Journal of the American Statistical Association.
Yu, X., Yao, J., and Xue, L. (2024).
"Power enhancement for testing multi-factor asset pricing models via Fisher's method" - link, pdf
Journal of Econometrics.
Liu, W., Yu, X., Zhong, W., and Li, R. (2024).
"Projection test for mean vector in high-dimensions" - link, pdf, code
Journal of the American Statistical Association.
Yu, X., Li, D., Xue, L., and Li, R. (2023).
"Power-enhanced simultaneous test of high-dimensional mean vectors and covariance matrices with application to gene-set testing" - link, pdf, code
Journal of the American Statistical Association.
Liu, W., Yu, X., and Li, R. (2022).
"Multiple-splitting projection test for high dimensional mean vectors" - link, pdf, code
Journal of Machine Learning Research.
# Luo, W., # Xue, L., # Yao, J., and # Yu, X. (2022).
"Inverse moment methods for sufficient forecasting using high-dimensional predictors" - link, pdf, code
Biometrika.
Yu, X., Yao, J., and Xue, L. (2022).
"Nonparametric estimation and conformal inference of sufficient forecasting with a diverging number of factors" - link, pdf, code
[ Winner of 2018 ASA Business and Economic Statistics Best Student Paper Award ].
Journal of Business & Economic Statistics.
Data Science & Applications:
Xu, G., Yu, H., Wei, L., Liu, Y., Liu, D., Xu, C., Li, J., Abbasi, A., Xiong, J.,Yu, X., Zheng, Z., Shi, Y., and Qin, R. (2026).
"Assessing the use of automatic speech recognition and large language models for individuals with language impairments" - link
Nature Communications. [Impact factor: 18.1]
# Zhang, C. , # Yu, X., and Zhang, B. (2024).
"Assessment of supervised longitudinal learning methods: Insights from predicting low birth weight and very low birth weight using prenatal ultrasound measurements" - link
Computers in Biology and Medicine. [Impact factor: 6.3]
# Nandy, P. , # Yu, X., Liu, W., Tu, Y., Basu, K., and Chatterjee, S. (2023).
"Generalized causal tree for uplift modeling" - link, code
IEEE BigData 2023. [Acceptance rate: 17.5%]
Liu, W., Yu, X., Mao, J., Wu, X., and Dyer, J. (2023).
"Quantifying the effectiveness of marketing campaigns: a bootstrap proportion test for brand lift testing" - link, code
CIKM 2023. [Acceptance rate: 24%]
Preprints:
@ Gu, Y., Yang, H., and Yu, X. - SSRN
"A new panel Granger causality test in heterogeneous panels with cross-sectional dependence. "
@ Luo, Z., Han, Y., and Yu, X. - Arxiv
"Factor augmented supervised learning with text embeddings"
Xing, Z., and Yu, X. - Arxiv
"NetworkNet: A deep neural network approach for random networks with sparse nodal attributes and complex nodal heterogeneity. "