English(EN) Information-Theoretic Bounds for Sparse Covariance Estimation in the Vertical-Split Distributed Model

稀疏协方差估计界限显示效率提升

作者 PulseAugur 编辑部 · [2 个来源] · 2026-06-05 10:25

研究人员为分布式垂直划分模型中的稀疏协方差估计开发了新的信息论界限。他们的工作表明，与密集矩阵相比，对交叉协方差矩阵施加稀疏性可以显著降低通信和样本复杂度。这种改进在1稀疏情况下尤为显著，提供了指数级的效率提升。 AI

影响为处理稀疏数据的分布式机器学习算法建立了理论效率优势。

排序理由该集群包含一篇详细介绍统计估计问题新理论界限的学术论文。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

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arXiv stat.ML TIER_1 English(EN) · Jing Yee Tan, Guangyue Han · 2026-06-08 04:00

垂直划分分布式模型中稀疏协方差估计的信息论界限

arXiv:2606.07124v1 Announce Type: cross Abstract: We study the minimax estimation error for distributed covariance matrix estimation in the vertical-split (feature-split) setting, where two agents each observe different coordinates of $m$ i.i.d. sub-Gaussian samples and communica…
arXiv stat.ML TIER_1 English(EN) · Guangyue Han · 2026-06-05 10:25

垂直划分分布式模型中稀疏协方差估计的信息论界限

We study the minimax estimation error for distributed covariance matrix estimation in the vertical-split (feature-split) setting, where two agents each observe different coordinates of $m$ i.i.d. sub-Gaussian samples and communicate a limited number of bits to a central server. W…