Publication

[14] Ke Sun, Yingnan Zhao, Enze Shi, Yafei Wang, Xiaodong Yan, Bei Jiang, and Linglong Kong. (2025) The Benefits of Being Categorical Distributional: Uncertainty-aware Regularized Exploration in Reinforcement Learning. ICML Workshop on Exploration in AI Today.

[13] Ce Zhang, Yixin Han, Yafei Wang, Xiaodong Yan, Linglong Kong, Ting Li, and Bei Jiang. (2025). Differentially Private Analysis for Binary Response Models: Optimality, Estimation, and Inference. In Forty-Second International Conference on Machine Learning (ICML).

[12] Yafei Wang, Bo Pan, Mei Li, Jianya Lu, Lingcheng Kong, Bei Jiang and Linglong Kong. (2024). Sample average approximation for conditional stochastic optimization with dependent data. In Forty-first International Conference on Machine Learning (ICML). [PDF]

[11] Yafei Wang, Bei Jiang, Linglong Kong, and Zhongzhan Zhang. (2024). M-estimation for varying coefficient models with a functional response in a reproducing kernel Hilbert space. Bernoulli 30, no. 3: 1998-2025. [PDF]

[10] Yafei Wang, Tingyu Lai, Bei Jiang, Linglong Kong, and Zhongzhan Zhang. (2022). Functional linear regression for incomplete functional data. Springer Book Series on Advances and Innovations in Statistics and Data Science. [PDF]

[9]  Yafei Wang, Bo Pan, Wei Tu, Peng Liu, Bei Jiang, Chao Gao, Wei Lu, Shangling Jui, and Linglong Kong. Sample average approximation for stochastic optimization with dependent data: performance guarantee and traceability. (2022). In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, no. 4, pp. 3859-3867.  [PDF]

[8] Ke Sun*, Yafei Wang*, Yi Liu, Yingnan Zhao, Bo Pan, Shangling Jui, Bei Jiang, and Linglong Kong. (2021). Damped Anderson mixing for deep reinforcement learning: acceleration, convergence, and stabilization. Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS). (∗: equal contributions in alphabetical order) [PDF]

[7] Tingyu Lai, Zhongzhan Zhang, and Yafei Wang. (2021). A kernel-based test for conditional mean independence. Computational Statistics & Data Analysis. [PDF]

[6] Tingyu Lai, Zhongzhan Zhang, Yafei Wang, and Linglong Kong. (2020). Testing independence of functional variables by angle covariance. Journal of Multivariate Analysis. [PDF]

[5] Tong Su, Yafei Wang, Yi Liu, William G. Branton, Eugene Asahchop, Christopher Power, Bei Jiang, Linglong Kong, and Niansheng Tang. (2020).Sparse multicategory generalized distance weighted discrimination in ultra-high dimensions. Entropy, 22(11):1-33.[PDF]

[4] Tingyu Lai, Zhongzhan Zhang, and Yafei Wang. (2020). Testing independence and goodness-of-fit jointly for functional linear models. Journal of the Korean Statistical Society, 1-23. [PDF]

[3]  Yafei Wang, Linglong Kong, Bei Jiang, Xingcai Zhou, Shimei Yu, Li Zhang, and Giseon Heo. (2019). Wavelet-based lasso in functional linear quantile regression. Journal of Statistical Computation and Simulation, 89(6): 1111–1130. [PDF]

[2] Yafei Wang, Tianfa Xie, and Zhongzhan Zhang. (2018). Partial functional linear models with ARCH errors. Open Journal of Statistics, 08(2):345–361. [PDF]

[1]  Yafei Wang, Jiang Du, and Zhongzhan Zhang. (2017). Partial functional linear model with dependent errors. Acta Mathematicae Applicatae Sinica, 40(1):49-65.  [PDF]