(CA) Adaptive deep nonparametric regression from dependent data under covariate shift

新的深度学习方法解决相关数据和协变量偏移下的回归问题

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-23 04:00

研究人员开发了一种新颖的稀疏惩罚深度神经网络（SPDNN）估计器，旨在解决协变量偏移和相关数据下的非参数回归挑战。该方法利用广义 Bernstein 型不等式和两步预训练程序来估计密度比和回归函数。提出的 SPDNN 估计器实现了非渐近误差界，并能自适应地获得各种数据模型（包括时间序列）的 minimax 最优收敛速率。 AI

影响引入了一种新的统计方法，可以提高机器学习模型在数据分布变化的实际场景中的鲁棒性。

排序理由详细介绍机器学习新统计方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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报道来源 [1]

arXiv stat.ML TIER_1 (CA) · William Kengne, Ehud Mossa Ockegna · 2026-07-23 04:00

来自依赖数据在协变量偏移下的自适应深度非参数回归

arXiv:2607.20309v1 Announce Type: new Abstract: Covariate shift often occurs because, in many real applications, the source and the target observations may be generated from different distributions. In this case, the standard metric under the source distribution is not appropriat…

本文如何被排名

报道来源 [1]

来自依赖数据在协变量偏移下的自适应深度非参数回归

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