Fan, X.#$, Ma, F.#$, Leng, C., Wu, W.*. Low-rank graphon learning for networks. NeurIPS 2025
Luo, T.#$, Fan, X.#$, Wu, W.*. Simultaneous statistical inference for off-policy evaluation in reinforce-ment learning. NeurIPS 2025
Bai, L.#, Wu, W.*. Uniform variance reduced simultaneous inference of time-varying correlation net-work. IEEE Transactions on Information Theory, accept.
Han, Y., Wu, W.*, Zhang, W. (2025) A New Approach for Homogeneity Pursuit in Short Panel Data Analysis, Journal of the American Statistical Association, theory and methods.
Luo, T. #，Wu, W.*（2025）Simultaneous inference for monotone and smoothly time-varying functions under complex temporal dynamics, Journal of the American Statistical Association, theory and methods.
Asaadi, M. , Yang, F. *, Wu, W.* (2025) Operational zone-specific univariate alarm design for incipient faults，Journal of Process Control.
Fan, X.#, Li, B.#, Leng, C.*, Wu, W.* Learning Changes in Graphon Attachment Network Models. ICML2025
Huang, Y., Wang, C., Tang, J., Wu, W.*, Xi, R.* A Generic Family of Graphical Models: Diversity, Efficiency, and Heterogeneity. ICML 2025
Chen, X., & Huang, K. , & Wu, W.*, & Jiang, H. *(2025) Detecting Multiple Changepoints by Exploiting Their Spatiotemporal Correlations: A Bayesian Hierarchical Approach. INFORMS Journal on Data Science, Accept
Dette, H., & Wu, W* (2024+) Confidence surfaces for the mean of locally stationary functional time series, Statistica Sinica, to appear
Wu, W*., Olhede, S., & Wolfe, P. (2024+) Tractably Modelling Dependence in Networks Beyond Exchangeability, Bernoulli, to appear
Bai,L#.&Wu,W.*(2024+). Difference-based covariance matrix estimate in time series nonparametric regression with applications to specification tests, Biometrika, to appear.
Bai,L#.&Wu,W.*(2023+). Detecting long-range dependence for time-varyingl inear models, Bernoulli, to appear.
Wu,W.&Zhou,Z.*(2023+). Multiscale jump testing and estimation under complex temporal dynamics, Bernoulli, to appear.
Dhar,S.S.&Wu,W.*(2023). Comparing time varying regression quantiles under shift invariance, Bernoulli, 29(2):1527-1554.
Dette,H.,&Wu,W.*(2022).Prediction in locally stationary time series, Journal of Business & Economic Statistics, 40(1), 370-381.
Dette, H., Dhar, S.S. & Wu,W.* (2021) . Identifying shifts between two regression curves, Annals of the Institute of Statistical Mathematics 1-35.
Dette,H.*,&Wu,W. (2019). Detecting relevant changes in the mean of a non-stationary process. The Annals of Statistics, 47(6), 3578–3608. (alphabetical authorship)
Wu,W.*,&Zhou,Z. (2018). Gradient-based structural change detection for nonstationary time series M-estimation. The Annals of Statistics, 46(3), 1197-1224.
Wu, W.*, & Zhou, Z. (2018). Simultaneous quantile inference for non-stationary long-memory time series. Bernoulli, 24(4A), 2991-3012.
Dette, H., Wu, W.* & Zhou, Z. (2018). Change point analysis of correlation in non-stationary time series. Statistica Sinica, 29(2), 611-644.
Wu, W.*, & Zhou, Z. (2017). Nonparametric inference for time-varying coefficient quantile regression. Journal of Business & Economic Statistics, 35(1), 98-109.
