English(EN) Do we really need the Rademacher complexities?

新的学习过程实现了通用的样本复杂度界限

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-11 05:00

研究人员开发了一种新的学习过程，即使在重尾情况下，也能在凸学习问题中实现最先进的样本复杂度界限。该方法利用最优均值估计技术和Talagrand的通用链式方法，推导出了与高斯设置中发现的速率相匹配的样本复杂度上限。这代表了通用凸学习问题的一个重要通用性结果，表明具有相同L2结构的 সমস্যা 问题的样本复杂度界限可能相似，而与分布尾部行为无关。 AI

影响这项研究通过为训练所需数据提供更严格的界限，有可能带来更高效的学习算法，尤其是在数据分布不理想的情况下。

排序理由详细介绍机器学习新理论成果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Daniel Bartl, Shahar Mendelson · 2026-11-11 05:00

我们真的需要Rademacher复杂度吗？

arXiv:2502.15118v2 Announce Type: replace-cross Abstract: We study the fundamental problem of learning with respect to the squared loss in a convex class. In the subgaussian setting, the sharpest known sample complexity bounds are characterised via fixed points associated with th…

本文如何被排名

报道来源 [1]

我们真的需要Rademacher复杂度吗？

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