Publications

(* corresponding author, # co-first author)

Submitted Papers

• Ren, M., He, X., & Wang, J. Structural transfer learning of non-Gaussian DAG. [arXiv] [R package]
  This work won the Junior Researcher Honorable Mention Award from the ICSA China Conference.
• Ren, M., Zhang, Q., Zhang, S., & Ma, S. Gaussian graphical model-based hierarchical cancer heterogeneity analysis via integrating pathological imaging and omics data.
• Zhang, F., Zhang, S., Ma, S., & Ren, M.* Subgroup analysis with double heterogeneity.
  This work won the Top Ten Papers of the National Graduate Statistics Forum held by RUC.
• Li, X., Zhang, S., Ren, M., & Zhang, Q. Integrative learning of linear non-Gaussian directed acyclic graphs.

Publications

• Zhang, Q., Zhou, J., & Ren, M.* (2025+). Network embedding-based directed community detection with unknown community number. Journal of Computational and Graphical Statistics, In press.
• Zhu, G., Zhang, S., & Ren, M.* (2025). Conditional Generative Learning from Invariant Representations in Multi-Source: Robustness and Efficiency.Conditional Generative Learning from Invariant Representations in Multi-Source: Robustness and Efficiency. The 28th International Conference on Artificial Intelligence and Statistics.
• Ren, M., Zhen, Y., & Wang, J. (2024). Transfer learning for tensor Gaussian graphical models. Journal of Machine Learning Research, 25(396), 1-40. [Article] [R package]
• Han, W., Zhang, S., Ma, S., & Ren, M.* (2024). Information‐incorporated sparse hierarchical cancer heterogeneity analysis. Statistics in Medicine, 43(11), 2280-2297. [Article] [Codes]
• Zhang, F., Zhang, S., Li, S. M., & Ren, M.* (2024). Matrix regression heterogeneity analysis. Statistics and Computing, 34(3), 95. [Article][Codes]
• Ren, M., Zhang, S., & Wang, J. (2023). Consistent estimation of the number of communities via regularized network embedding. Biometrics, 79(3), 2404-2416. [Article][Codes] [R package]
• Ren, M., Zhang, Q., Zhang, S., Zhong, T., Huang, J., & Ma, S. (2022). Hierarchical cancer heterogeneity analysis based on histopathological imaging features. Biometrics, 78(4), 1579-1591. [Article] [Codes] [R package]
• Ren, M., Zhang, S., Zhang, Q., & Ma, S. (2022). Gaussian graphical model-based heterogeneity analysis via penalized fusion. Biometrics, 78(2), 524-535. [Article] [Codes] [R package]
• Ren, M., Zhang, S., Ma, S., & Zhang, Q. (2022). Gene-environment interaction identification via penalized robust divergence. Biometrical Journal, 64(3), 461-480. [Article] [Codes]
• Ren, M., Zhang, S., Zhang, Q., & Ma, S. (2021). HeteroGGM: an R package for Gaussian graphical model-based heterogeneity analysis. Bioinformatics, 37(18), 3073-3074. [Article] [Github] [CRAN]
• Ren, M., Zhang, S., & Zhang, Q. (2021). Robust high-dimensional regression for data with anomalous responses. Annals of the Institute of Statistical Mathematics, 73, 703-736. [Article]
• Liu, Y., Ren, M., & Zhang, S. (2021). Empirical likelihood test for regression coefficients in high dimensional partially linear models. Journal of Systems Science and Complexity, 34(3), 1135-1155. [Article]
• Li, S., Ran, A., Kang, M., Yang, X., Ren, M., Wei, S., … & Anyang Childhood Eye Study Group. (2022). Effect of text messaging parents of school-aged children on outdoor time to control myopia: a randomized clinical trial. JAMA Pediatrics, 176(11), 1077-1083. [Article]
• Li, S. #, Ren, M. #, Gan, J., Zhang, S., Kang, M., Li, H., … & Anyang Childhood Eye Study Group. (2022). Machine learning to determine risk factors for myopia progression in primary school children: the Anyang childhood eye study. Ophthalmology and Therapy, 11(2), 573-585. [Article]