3 citations · 3 across the 3 of their papers we have counts for
5 papers · 1 filter
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…
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).…
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…
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…
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…