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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…
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…
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…