English(EN) Learning by Compression: Risk Estimates Attain the Law-of-Large-Numbers Rate

新研究论文详细介绍了基于压缩的学习算法的风险界限

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-12 04:00

一篇题为“通过压缩学习：风险估计达到大数定律速率”的新论文已在arXiv上发表。该研究引入了一个通用框架，使用压缩方案为学习算法推导数据依赖的风险界限。它证明了现有风险界限中的对数因子并非固有，证明了在每个固定置信水平下，风险都受k/N + O(1/sqrt(N))的约束。该论文还探讨了首项k/N的必要性及其与经验风险的关系，确定了经验风险保持O(1/sqrt(N))收敛速率的条件。 AI

影响这项研究通过改进理论界限，可能带来更准确的机器学习算法风险评估。

排序理由该集群包含一篇发表在arXiv上的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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arXiv stat.ML TIER_1 English(EN) · Simone Garatti, Marco C. Campi · 2026-10-12 04:00

通过压缩学习：风险估计达到大数定律速率

arXiv:2610.13442v1 Announce Type: new Abstract: Compression schemes provide a general framework for deriving data-dependent risk bounds for learning algorithms. For learning rules based on preferent compression, existing results upper-bound the risk in terms of a sample-dependent…

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通过压缩学习：风险估计达到大数定律速率

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