English(EN) New Lower Bound and Upper Bounds on the Regret for Online Sparse Linear Regression

在线稀疏线性回归遗憾的新界限已确立

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-09 04:00

研究人员为在线稀疏线性回归（一种算法选择有限数量的属性进行预测的复杂问题）建立了新的理论界限。这项工作首次为该任务的最小最大遗憾设定了下界，并提出了一种算法，即使在没有正则性假设的情况下也能实现改进的上界。这些发现为理解在线稀疏线性回归所涉及的信息论复杂度提供了更清晰的认识。 AI

影响为理解在线学习算法的复杂度提供了理论基础。

排序理由详细介绍机器学习问题新理论界限的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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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Signal score: 14 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
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arXiv cs.LG TIER_1 English(EN) · Xiaofeng Cao, Junfan Li, Langzhang Liang, Mingwei Xu, Xiao Zhang · 2026-10-09 04:00

在线稀疏线性回归的新下界和上界

arXiv:2610.11551v1 Announce Type: new Abstract: We study online sparse linear regression (OSLR) where any algorithm is restricted to accessing only $b$ out of $d$ attributes per instance for prediction and $b_0\geq 0$ additional attributes after prediction, which was proved to be…