学术报告：张瑜 中南财经政法大学

主讲人：张瑜 中南财经政法大学

题 目： The Decentralized Distributed Sparsity Learning with Expectile Regression

-ssive models

时间：2025年10月18日 14:00-15:30

地点：VSport体育官网新校园 B513

摘要：Decentralized learning has gained increasing attention within the realm of big data distributed computing, owing to its merits in computational efficiency, data privacy protection and system stability. In the context of decentralized distributed learning, this paper proposes a sparse estimation method for expectile regression, leveraging the the asymmetric least squares loss and L1 penalty. An ADMM-LAMM algorithm with a linear convergence rate is also outlined.Moreover, this paper establishes that the proposed estimator attains an approximately Oracle convergence rate and presents theoretical findings related to the recovery of the sparse support set. Lastly, numerical simulations and real data analysis are conducted to showcase the robustness and efficacy of the proposed methodology in handling heavy-tailed, heterogeneous high-dimensional data.