Generalization of Distributed Huber Regression Algorithms with Uniformly Ergodic Markov Chain Samples

doi:10.13438/j.cnki.jdzk.2026.04.003

Abstract

Abstract: This paper investigates the generalization performance of distributed Huber regression with samples generated from uniformly ergodic Markov chains.We first construct a distributed Huber regression algorithm applicable to uniformly ergodic Markov chain samples.Relying on statistical learning theory,we further derive the convergence rates and generalization bounds for the proposed distributed Huber regression with Markov chain sampling.Theoretical proofs demonstrate that the distributed Huber regression algorithm achieves a fast convergence rate even in the non-independent and identically distributed (non-i.i.d.) sample setting.

Key words: distributed, huber regression algorithm, uniformly ergodic Markov chain samples, convergence rates

LI Ke, JIANG Hongwei. Generalization of Distributed Huber Regression Algorithms with Uniformly Ergodic Markov Chain Samples[J]. Journal of Jishou University(Natural Sciences Edition), 2026, 47(4): 11-18.

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