Ye Tian (田野)

By topic
By year

Data aggregation/Transfer learning/Multi-task learning

  • Tian, Y., Gu, Y., & Feng, Y. (2025). Learning from Similar Linear Representations: Adaptivity, Minimaxity, and Robustness. Journal of Machine Learning Research. (just accepted)
    [Link] [Code]
  • Tian, Y., Weng, H., & Feng, Y. (2024) Towards the Theory of Unsupervised Federated Learning: Non-asymptotic Analysis of Federated EM Algorithms. In Forty-first International Conference on Machine Learning (ICML).
    [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]
  • Tian, Y., Weng, H., Xia, L., & Feng, Y. (2022). Unsupervised Multi-task and Transfer Learning on Gaussian Mixture Models. arXiv preprint arXiv:2209.15224.
    [Link] [R package]

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., & 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]