English(EN) Complexity of Non-Log-Concave Sampling in Fisher Information

新算法提高了非对数凹形分布的采样复杂性

作者 PulseAugur 编辑部 · [2 个来源] · 2026-05-15 11:20

研究人员开发了一种从非对数凹形分布采样的算法，改进了先前的方法。该算法利用了对数凹形采样方面的最新进展，并使用受限高斯预言机（RGO）实现。这种方法在相对Fisher信息方面提供了与对数凹形采样维度依赖性相匹配的复杂性保证，标志着非对数凹形分布的改进。 AI

影响引入了一种更有效的复杂分布采样方法，可能有利于机器学习模型的训练和分析。

排序理由该集群包含一篇详细介绍机器学习采样领域新算法和理论分析的学术论文。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

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arXiv cs.LG TIER_1 English(EN) · Andre Wibisono · 2026-05-15 11:20

Fisher信息中非对数凹面采样的复杂性

We study the query complexity of obtaining a relative Fisher information guarantee for sampling from a log-smooth non-log-concave distribution; this is a sampling analog of finding an approximate stationary point in optimization. Our algorithm is based on the proximal sampler, wh…
arXiv stat.ML TIER_1 English(EN) · Sinho Chewi, Andre Wibisono · 2026-05-18 04:00

Fisher信息中非对数凹面采样的复杂性

arXiv:2605.15859v1 Announce Type: cross Abstract: We study the query complexity of obtaining a relative Fisher information guarantee for sampling from a log-smooth non-log-concave distribution; this is a sampling analog of finding an approximate stationary point in optimization. …