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新算法解决机器学习中的约束采样问题 · 跟踪到2个来源

作者 PulseAugur 编辑部 · [3 个来源] ·

两篇新研究论文介绍了用于机器学习中约束采样的创新算法。第一篇论文《Reflected Anchored Langevin Algorithms》提出了一种名为RALD的方法，该方法可以收敛到约束域上的不可微分目标，克服了现有的一阶Langevin算法的局限性。第二篇论文《Penalized Nonreversible Langevin for Constrained Sampling》提出了一种结合平方距离惩罚和斜对称扰动的算法，用于从紧凸集上的分布进行采样，并展示了在特定模型中加速的潜力。 AI

影响 引入了新的采样技术，可能提高机器学习模型在受限环境中的效率和适用性。

排序理由 两篇在arXiv上发表的学术论文，详细介绍了机器学习中约束采样的算法。

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新算法解决机器学习中的约束采样问题 · 跟踪到2个来源

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两篇在arXiv上发表的学术论文，详细介绍了机器学习中约束采样的算法。
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报道来源 [3]

  1. arXiv cs.LG TIER_1 English(EN) · Changwei Tu, Xiaoyu Wang, Yingli Wang, Xicheng Zhang, Lingjiong Zhu ·

    Reflected Anchored Langevin Algorithms

    arXiv:2610.09522v1 Announce Type: cross Abstract: First order Langevin algorithms for constrained sampling in machine learning, such as projected Langevin Monte Carlo which are based on discretizations of reflected Langevin dynamics, require differentiable log densities that limi…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    Reflected Anchored Langevin Algorithms

    First order Langevin algorithms for constrained sampling in machine learning, such as projected Langevin Monte Carlo which are based on discretizations of reflected Langevin dynamics, require differentiable log densities that limits their applicability. This paper introduces refl…

  3. arXiv cs.LG TIER_1 English(EN) · Pervez Ali, Weihao Dong, Xiaoyu Wang ·

    Penalized Nonreversible Langevin for Constrained Sampling

    arXiv:2609.25381v2 Announce Type: replace-cross Abstract: We propose penalized nonreversible Langevin algorithms for sampling from $\pi(x)\propto e^{-f(x)}\mathbf 1_{\mathcal C}(x)$, where $\mathcal C\subset\mathbb R^d$ is a compact convex set. The algorithms combine a squared di…

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