Papers

2024

Towards the Theory of Unsupervised Federated Learning: Non-asymptotic Analysis of Federated EM Algorithms

Ye Tian, Haolei Weng, Yang Feng
Preprint: arXiv: 2310.15330 (just accepted at ICML 2024)
[PDF]

Federated Transfer Learning with Differential Privacy

Mengchu Li, Ye Tian, Yang Feng, Yi Yu
Preprint: arXiv: 2403.11343
[PDF]

2023

Comments on: Statistical inference and large-scale multiple testing for high-dimensional regression models

Ye Tian, Yang Feng
Test, 32(4), 1172-1176.
[PDF]

Learning from Similar Linear Representations: Adaptivity, Minimaxity, and Robustness

Ye Tian, Yuqi Gu, Yang Feng
Preprint: arXiv: 2303.17765
[PDF] [code]

L1-penalized Multinomial Regression: Estimation, inference, and prediction, with an application to risk factor identification for different dementia subtypes

Ye Tian, Henry Rusinek, Arjun V. Masurkar, Yang Feng
Preprint: arXiv: 2302.02310
[PDF] [R package]

2022

Unsupervised Multi-task and Transfer Learning on Gaussian Mixture Models

Ye Tian, Haolei Weng, Yang Feng
Preprint: arXiv: 2209.15224
[Winned the student travel award at ICSDS 2022]
[PDF] [R package]

Transfer Learning under Generalized Linear Models

Ye Tian, Yang Feng
Journal of the American Statistical Association (2022): 1-30
[PDF] [R package]

2021

Neyman-Pearson Multi-class Classification via Cost-sensitive Learning

Ye Tian, Yang Feng
Preprint: arXiv: 2111.04597
[PDF] [R package]

RaSE: A Variable Screening Framework via Random Subspace Ensembles

Ye Tian, Yang Feng
Journal of the American Statistical Association (2021): 1-12
[PDF] [R package] [R code]

A Multilayer Correlated Topic Model

Ye Tian
Preprint: arXiv:2101.02028
[PDF]

RaSE: Random Subspace Ensemble Classification

Ye Tian, Yang Feng
Journal of Machine Learning Research, 22(45), 1-93
[PDF] [R package]

2019

THORS: An Efficient Approach for Making Classifiers Cost-Sensitive

Ye Tian, Weiping Zhang
IEEE Access, 7, 97704-97718.
[PDF]