English(EN) Variance Reduction for Stochastic Gradient Generalized Non-reversible Langevin Monte Carlo Algorithms

新的蒙特卡洛算法降低了随机梯度方法的方差

作者 PulseAugur 编辑部 · [2 个来源] · 2026-06-27 08:25

研究人员开发了用于随机梯度广义不可逆Langevin蒙特卡洛算法的新方差缩减技术。这些方法旨在提高广义不可逆Langevin动力学估计器的准确性，特别是在步长趋于零的情况下。在贝叶斯回归任务上的数值实验表明，与可逆方案相比，提出的不可逆方案持续降低了均方根误差。 AI

影响这些算法改进可以提高用于复杂机器学习模型中的采样方法的效率和准确性。

排序理由该集群包含一篇详细介绍机器学习新算法和理论发现的学术论文。

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arXiv stat.ML TIER_1 English(EN) · Bingye Ni, Xiaoyu Wang, Yingli Wang, Lingjiong Zhu · 2026-06-30 04:00

随机梯度广义不可逆Langevin蒙特卡洛算法的方差缩减

arXiv:2606.28808v1 Announce Type: new Abstract: We study the leading-order fluctuation of stochastic gradient Euler-Maruyama estimators for generalized non-reversible Langevin dynamics. Under structural assumptions tailored to the small-stepsize central limit theorem and under an…
arXiv stat.ML TIER_1 English(EN) · Lingjiong Zhu · 2026-06-27 08:25

随机梯度广义不可逆Langevin蒙特卡洛算法的方差缩减

We study the leading-order fluctuation of stochastic gradient Euler-Maruyama estimators for generalized non-reversible Langevin dynamics. Under structural assumptions tailored to the small-stepsize central limit theorem and under an unbiased stochastic gradient oracle, we prove t…