17 citations · 49 across the 13 of their papers we have counts for
14 papers
Sufficient Dimesion Reduction via Generalized Stein's Lemma
Ye Tian
Sufficient dimension reduction (SDR) seeks the minimal subspace of the predictors that captures the full conditional distribution of the response, which is known as the central sub…
Contaminated Multi-task Learning with Heterogeneity: Fundamental Limits and Optimal Algorithms
Ye Tian, Mengchu Li, Marco Avella Medina
Integrating information across related tasks can improve estimation and prediction in transfer, multi-task, and federated learning, but contamination and heterogeneity make robust…
Multi-Source Transfer Learning of Sparse Single-Index Models
Ye Tian
Transfer learning leverages knowledge from related source domains to improve learning in a target domain. Recent theoretical advances cover a broad range of regression settings wit…
Federated Transfer Learning with Differential Privacy
Mengchu Li, Ye Tian, Yang Feng +1
Federated learning has emerged as a powerful framework for analysing distributed data, yet two challenges remain pivotal: heterogeneity across sites and privacy of local data. In t…
Towards the Theory of Unsupervised Federated Learning: Non-asymptotic Analysis of Federated EM Algorithms
Ye Tian, Haolei Weng, Yang Feng
While supervised federated learning approaches have enjoyed significant success, the domain of unsupervised federated learning remains relatively underexplored. Several federated E…
Learning from Similar Linear Representations: Adaptivity, Minimaxity, and Robustness
Ye Tian, Yuqi Gu, Yang Feng
Representation multi-task learning (MTL) has achieved tremendous success in practice. However, the theoretical understanding of these methods is still lacking. Most existing theore…
-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 +1
High-dimensional multinomial regression models are very useful in practice but have received less research attention than logistic regression models, especially from the perspectiv…
Robust Unsupervised Multi-task and Transfer Learning on Gaussian Mixture Models
Ye Tian, Haolei Weng, Lucy Xia +1
Unsupervised learning has been widely used in many real-world applications. One of the simplest and most important unsupervised learning models is the Gaussian mixture model (GMM).…
Neyman-Pearson Multi-class Classification via Cost-sensitive Learning
Ye Tian, Yang Feng
Most existing classification methods aim to minimize the overall misclassification error rate. However, in applications such as loan default prediction, different types of errors c…
Transfer Learning under High-dimensional Generalized Linear Models
Ye Tian, Yang Feng
In this work, we study the transfer learning problem under high-dimensional generalized linear models (GLMs), which aim to improve the fit on target data by borrowing information f…
RaSE: A Variable Screening Framework via Random Subspace Ensembles
Ye Tian, Yang Feng
Variable screening methods have been shown to be effective in dimension reduction under the ultra-high dimensional setting. Most existing screening methods are designed to rank the…
A Multilayer Correlated Topic Model
Ye Tian
We proposed a novel multilayer correlated topic model (MCTM) to analyze how the main ideas inherit and vary between a document and its different segments, which helps understand an…
RaSE: Random Subspace Ensemble Classification
Ye Tian, Yang Feng
We propose a flexible ensemble classification framework, Random Subspace Ensemble (RaSE), for sparse classification. In the RaSE algorithm, we aggregate many weak learners, where e…
THORS: An Efficient Approach for Making Classifiers Cost-sensitive
Ye Tian, Weiping Zhang
In this paper, we propose an effective THresholding method based on ORder Statistic, called THORS, to convert an arbitrary scoring-type classifier, which can induce a continuous cu…