arXiv subjects

Changxin Yang

Publications and source records attributed to Changxin Yang.

3 recordsLinked to original sources

arXiv2026

Decentralized Sparse Quantile Regression

Distributed statistical learning has recently attracted significant attention owing to its computational efficiency and scalability in large-scale systems. However, the non-smooth quantile loss poses substantial challenges to the development of efficient decentralized algorithms for quantile regression. Existing methods may either suffer from slow convergence or rely on smoothing-based approximations that require additional stringent assumptions, particularly in high-dimensional settings. To address these issues, we propose a decentralized framework that integrates gradient tracking with iterative hard thresholding for sparse quantile estimation. The proposed method avoids smoothing approximations and relaxes the initialization requirement. Theoretically, we show that the proposed algorithm achieves linear convergence under stated regularity conditions and that the resulting estimator attains the same order of statistical accuracy as its centralized counterpart. We further develop a decentralized information criterion for selecting the working sparsity level and establish support screening guarantees. Numerical experiments and a real-data study illustrate the finite-sample performance of the proposed method.

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arXiv2025

Robust Clustered Federated Learning for Heterogeneous High-dimensional Data

Federated learning has attracted significant attention as a privacy-preserving framework for training personalised models on multi-source heterogeneous data. However, most existing approaches are unable to handle scenarios where subgroup structures coexist alongside within-group heterogeneity. In this paper, we propose a federated learning algorithm that addresses general heterogeneity through adaptive clustering. Specifically, our method partitions tasks into subgroups to address substantial between-group differences while enabling efficient information sharing among similar tasks within each group. Furthermore, we integrate the Huber loss and Iterative Hard Thresholding (IHT) to tackle the challenges of high dimensionality and heavy-tailed distributions. Theoretically, we establish convergence guarantees, derive non-asymptotic error bounds, and provide recovery guarantees for the latent cluster structure. Extensive simulation studies and real-data applications further demonstrate the effectiveness and adaptability of our approach.

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arXiv2024

Distributed Iterative Hard Thresholding for Variable Selection in Tobit Models

While extensive research has been conducted on high-dimensional data and on regression with left-censored responses, simultaneously addressing these complexities remains challenging, with only a few proposed methods available. In this paper, we utilize the Iterative Hard Thresholding (IHT) algorithm on the Tobit model in such a setting. Theoretical analysis demonstrates that our estimator converges with a near-optimal minimax rate. Additionally, we extend the method to a distributed setting, requiring only a few rounds of communication while retaining the estimation rate of the centralized version. Simulation results show that the IHT algorithm for the Tobit model achieves superior accuracy in predictions and subset selection, with the distributed estimator closely matching that of the centralized estimator. When applied to high-dimensional left-censored HIV viral load data, our method also exhibits similar superiority.

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