English(EN) The optimal information complexity of VC learning

新框架恢复VC学习的最优PAC保证

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

Steinke和Zakynthinou研究人员开发了一个使用条件互信息（CMI）分析学习算法信息复杂度的框架。他们的工作表明，通过这种CMI方法可以恢复VC类别的最优大概率近似正确（PAC）保证。具体来说，他们构建了一种随机学习算法，该算法实现了O(d)阶的已评估条件互信息（eCMI），其中“d”代表概念类的VC维度，从而在期望上实现了最优的泛化保证。 AI

影响这项研究推进了对学习算法复杂性和泛化保证的理论理解。

排序理由该集群包含一篇详细介绍机器学习新理论框架和算法的学术论文。[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) · Steve Hanneke, Juexiao Wang · 2026-10-09 04:00

VC学习的最优信息复杂度

arXiv:2610.10600v1 Announce Type: cross Abstract: Steinke and Zakynthinou(2020) introduces the Conditional Mutual Information (CMI) framework of analyzing the information complexity of learning algorithms based on algorithm-dependent information-theoretic quantities. We study one…

本文如何被排名

报道来源 [1]

VC学习的最优信息复杂度

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