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通过再生核希尔伯特空间中的函数迁移学习调整选择偏差AI 解读

As accessibility to different data sources increases, transfer learning becomes prevalent in improving statistical efficiency by integrating different datasets. In this article, we focus on the popular non-probability sampling from a finite population and propose a functional transfer learning method to adjust its selection bias through calibrating marginal means of functions in a reproducing kernel Hilbert space by incorporating a reference probability sample. Compared with…

具有预言性质与几何收敛性的通信高效分布式稀疏学习AI 解读

This article introduces two highly efficient distributed non-convex sparse learning algorithms. Our approach accommodates non-convexity in both the loss function and penalty, acknowledging the potential non-uniqueness of local minimizers due to the inherent non-convexity. The development of an algorithm that ensures convergence to a locally minimal solution with desired statistical properties becomes imperative in this context. To overcome this challenge, we propose a…

带稀疏惩罚的稳健个性化联邦学习AI 解读

Federated learning (FL) is an emerging topic due to its advantage in collaborative learning with distributed data. Due to the heterogeneity in the local data-generating mechanism, it is important to consider personalization when developing federated learning methods. In this work, we propose a personalized federated learning (PFL) method to address the robust regression problem. Specifically, we aim to learn the regression weight by solving a Huber loss with the sparse fused…

结合协变量信息的矩阵补全AI 解读

This article investigates the problem of matrix completion from the corrupted data, when the additional covariates are available. Despite being seldomly considered in the matrix completion literature, these covariates often provide valuable information for completing the unobserved entries of the high-dimensional target matrix A0. Given a covariate matrix X with its rows representing the row covariates of A0, we consider a column-space-decomposition model A0 = Xβ0 + B0,…