English(EN) Universal Bounds for Self-Normalized Kullback-Leibler Estimation

新估计器为 Kullback-Leibler 散度提供通用界限

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

研究人员开发了一种新的 Kullback-Leibler (KL) 散度估计器，该估计器基于归一化重要性权重。该估计器实现了通用的有限样本界限，这意味着其期望值最多可以超过目标-提议 KL 散度 log 2，而与样本量无关。该工作还表征了重要性加权重采样引起的分布与提议分布之间的 KL 散度，提供了无矩界限。这些发现可应用于使用 Soft Best-of-N 采样等技术对生成模型进行对齐以及在相对熵编码中的应用。 AI

排序理由学术论文发表在 arXiv 上，详细介绍了一种新的统计估计方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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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arXiv stat.ML TIER_1 English(EN) · Buu Phan, Lester Mackey, David Alvarez-Melis · 2026-10-12 04:00

自归一化Kullback-Leibler估计的通用界限

arXiv:2610.12851v1 Announce Type: new Abstract: This work studies an entropy-based estimator of Kullback-Leibler (KL) divergence constructed from normalized importance weights. We prove a universal finite-sample bound showing that its expectation can exceed the target-proposal KL…

本文如何被排名

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自归一化Kullback-Leibler估计的通用界限

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