Some Preprints( *: student coauthor)

• Ren, M., He, X. and Wang, H.  (2024+).  Structure transfer learning of non-Gaussian DAG with local similarity. Submitted.

• Wang, C*., Wang, C.*, He, X. and  Feng, X. (2024+)   Optimal Transfer Learning for Kernel-based Nonparametric Regression. Submitted.

Textbook

• Feng, X. and  He, X. (2023).  Distributed statistical computing.  China Renmin University Press.

Conferences( *: student coauthor)

• Wang, C*., Bing, X., He, X. and Wang, C*. (2024).  Towards Theoretical Understanding of Learning Large-scale Dependent Data via Random Features. ICML (Spotlight).

• Feng, X., He, X., Wang, C*., Wang, C*. and Zhang J. (2023).  Towards a Unified Analysis of Kernel-based Methods Under Covariate Shift. NeurIPS.

• Sui, Y*.,   He, X. and Bai, Y.  (2023).  Implicit Regularization in Over-Parameterized Support Vector Machine. NeurIPS. 

Journal Publications( *: student coauthor)

Graphical/network Analysis

• Deng, Y*.,  He, X., and Lv, S. (2024+).  Efficient learning of nonparametric directed acyclic graph with statistical guarantee. Statistica Sinica, to appear.

• Zhou, W., He, X., Zhong, W. and  Wang, J. (2022). Efficient Learning of Quadratic Variance Function Directed Acyclic Graphs via Topological Layers.  Journal of Computational and Graphical Statistics, 31(4):1269-1279.

• Zhang, J., He, X. and  Wang, J. (2022). Directed community detection with network embedding. Journal of the American Statistical Association (Theory and Methods), 117(540):1809-1819.

• Zhao, R., He, X.,  and Wang, J. (2022). Learning linear non-Gaussian directed acyclic graph with diverging number of nodes. Journal of Machine Learning Research, 23(269):1−34.

Kernel-based Methods

• Wang, C.*, Li, T., Zhang, X., Feng, X. and He., X. (2024+). Communication-Efficient Nonparametric Quantile Regression via Random Features. Journal of Computational and Graphical Statistics, to appear.

• He, X., Mao., X. and Wang, Z. (2024). Nonparametric augmented probability weighting with sparsity. Computational Statistics and Data Analysis, to appear.

• He, X., Ge, Y*. and Feng, X. (2023). Structure learning via unstructured kernel-based M-estimation. Electronic Journal of Statistics,  17(2): 2386-2415.

• Lv, S., He, X. and  Wang, J. (2023). Kernel-based estimation for partially functional linear model: Minimax rates and randomized sketches. Journal of Machine Learning Research, 24:1-38.

• Chen, F*.,  He, X. and Wang, J. (2021). Learning sparse conditional distribution: an efficient kernel-based approach. Electronic Journal of Statistics, 15(1), 1610-1635.

• He, X., Wang, J. and Lv, S. (2021). Efficient kernel-based variable selection with sparsistency. Statistica Sinica, 31, 2123-2151. 

• He, X., Lv, S. and Wang, J. (2020). Variable selection for classification with derivative-induced regularization. Statistica Sinica,  30(4), 2075-2103. 

• Xia, Y., Hou, Y, He, X, and Lv, S. (2020).  Learning rates for partially linear functional models with high dimensional scalar covariates. Communications on Pure and Applied Analysis, 8, 3917-3932.

• He, X. and Wang, J. (2020). Discovering model structure for partially linear models. Annals of the Institute of Statistical Mathematics, 72, 45-63.

• He, X., Xu, S. and Wang, J. (2019). Discussion on "Entropy Learning for Dynamic Treatment Regimes". Statistica Sinica, 29, 1658-1662.

• He, X., Wang, J. and Lv, S. (2018). Gradient-induced model-free variable selection with composite quantile regression. Statistica Sinica, 28, 1521-1538.

• Lv, S., He, X. and Wang, J. (2016). A unified penalized method for sparse additive quantile models: an RKHS approach.  Annals of the Institute of Statistical Mathematics, 69, 897-923.

Application in Medicine

• Zhang, B., Tian, J., Pei, S., Chen, Y.,  He, X., et al. (2019).  Machine Learning-Assisted System for Thyroid Nodule Diagnosis. Thyroid, 29, 858-867. 

• Zhang, B., He, X., et al. (2017).  Radiomic machine-learning classifiers for prognostic biomarkers of advanced nasopharyngeal Carcinoma.  Cancer letters, 403, 21-27. 

Acknowledgement

My research is supported in part by NSFC-11901375 and Shanghai Pujiang Program 2019PJC051.