English(EN) High-Magnetization Sampling at Low Temperatures: Ising Models and Bayesian Sparse Linear Regression

新的采样框架改进了Ising模型和贝叶斯回归

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

研究人员开发了在高维统计环境下采样问题的新框架，特别关注Ising模型和贝叶斯稀疏线性回归。这些框架允许在固定磁化Ising模型中改进采样器，即使在强外部场下的低温条件也能实现。此外，该工作减少了从贝叶斯稀疏线性回归中的高斯尖峰和板条后验采样所需的测量次数，改进了先前结果。 AI

影响引入了与机器学习和统计推断相关的采样技术的理论进展。

排序理由该条目是一篇学术论文，详细介绍了统计采样问题的新理论框架和算法改进。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Syamantak Kumar, Purnamrita Sarkar, Kevin Tian, Yusong Zhu · 2026-09-10 04:00

低温高磁化率采样：Ising模型与贝叶斯稀疏线性回归

arXiv:2609.08873v1 Announce Type: cross Abstract: Sparsity is a powerful structural resource in optimization and statistics. We develop frameworks for leveraging sparsity in sampling problems over the Hamming slice $\mathcal{X}_k^d:=\{\mathbf{x}\in\{\pm 1\}^d:|\{i:\mathbf{x}_i=1\…