Research

Data aggregation/Transfer learning/Multi-task learning

• Tian, Y., Weng, H., Xia, L., & Feng, Y. (2026). Robust Unsupervised Multi-task and Transfer Learning on Gaussian Mixture Models. Journal of the American Statistical Association, 1-14. [Link] [Preprint] [R package]
• Tian, Y., Li, M., & Avella Medina, M. (2026). Contaminated Multi-task Learning with Heterogeneity: Fundamental Limits and Optimal Algorithms. arXiv preprint arXiv:2607.02681. [Link]
• Liu, Z., Tian, Y., & Tang, W. (2026). Representation Learning with Blockwise Missingness and Signal Heterogeneity. arXiv preprint arXiv:2602.11511. [Link]
• Tian, Y., Gu, Y., & Feng, Y. (2025). Learning from Similar Linear Representations: Adaptivity, Minimaxity, and Robustness. Journal of Machine Learning Research, 26(187), 1-125. [Link] [Code]
• Tian, Y., Weng, H., & Feng, Y. (2024). Towards the Theory of Unsupervised Federated Learning: Non-asymptotic Analysis of Federated EM Algorithms. In Proceedings of the 41st International Conference on Machine Learning (ICML), PMLR 235, 48226-48279. [Link]
• Li, M., Tian, Y., Feng, Y., & Yu, Y. (2024). Federated Transfer Learning with Differential Privacy. arXiv preprint arXiv:2403.11343. [Link]
• Tian, Y., & Feng, Y. (2023). Transfer Learning under High-dimensional Generalized Linear Models. Journal of the American Statistical Association, 118(544), 2684-2697. [Link] [R package]
• Tian, Y., & Feng, Y. (2023). Comments on: Statistical Inference and Large-scale Multiple Testing for High-dimensional Regression Models. Test, 32(4), 1172-1176. [Link]

Neyman-Pearson classification/Classification error control

• Tian, Y., & Feng, Y. (2025). Neyman-Pearson Multi-class Classification via Cost-sensitive Learning. Journal of the American Statistical Association, 120(550), 1164-1177. [Link] [R package] [Code]
• Tian, Y., Rusinek, H., Feng, Y., & Vedvyas, A. (2022). Prediction of Dementia Subtypes by Machine Learning: Feasibility of Error Control. Alzheimer’s & Dementia, 18(S11), e069240. (Conference abstract) [Link] [Poster]
• Tian, Y., & Zhang, W. (2019). THORS: An Efficient Approach for Making Classifiers Cost-sensitive. IEEE Access, 7, 97704-97718. [Link]

High-dimensional statistics and general machine learning

• Tian, Y., Rusinek, H., Masurkar, A. V., & Feng, Y. (2024). ℓ₁-penalized Multinomial Regression: Estimation, Inference, and Prediction, with an Application to Risk Factor Identification for Different Dementia Subtypes. Statistics in Medicine, 43(30), 5711-5747. [Link] [R package] [Code]
• Tian, Y., & Feng, Y. (2023). RaSE: A variable Screening Framework via Random Subspace Ensembles. Journal of the American Statistical Association, 118(541), 457-468. [Link] [R package]
• Tian, Y., & Feng, Y. (2021). RaSE: Random Subspace Ensemble Classification. Journal of Machine Learning Research, 22(45), 1-93. [Link] [R package]