English(EN) Tight Sampling Complexity with stochastic gradient oracles in Fixed Dimensions

新研究详细介绍了对数凹形分布的紧凑采样复杂度

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-14 04:00

研究人员开发了一种新方法来分析使用随机梯度预言机对光滑、强对数凹形分布进行采样的复杂度。该研究为采样复杂度建立了紧凑界限，该界限同时适应条件数和精度参数。在无噪声设置下，此界限也简化为对条件数的对数依赖性。 AI

影响为可能用于AI模型训练的采样方法提供了理论基础。

排序理由该集群包含一篇学术论文，详细介绍了统计学中的一项新理论发现。[lever_c_demoted from research: ic=1 ai=0.7]

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 cs.LG TIER_1 English(EN) · Weiming Ou, Xiao Wang · 2026-09-14 04:00

固定维度下随机梯度预言机的紧凑采样复杂度

arXiv:2609.12590v1 Announce Type: cross Abstract: We investigate the stochastic-gradient query complexity of sampling smooth strongly log-concave distributions in any fixed Euclidean dimension. The potential is $\mu$-strongly convex and $L$-smooth, with an unknown mode in the bal…