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New algorithms tackle constrained sampling in machine learning · 2 sources tracked

By PulseAugur Editorial · [3 sources] ·

Two new research papers introduce novel algorithms for constrained sampling in machine learning. The first, "Reflected Anchored Langevin Algorithms," presents a method called RALD that can converge to non-differentiable targets on constrained domains, overcoming limitations of existing first-order Langevin algorithms. The second paper, "Penalized Nonreversible Langevin for Constrained Sampling," proposes algorithms that combine a squared distance penalty with skew-symmetric perturbations to sample from distributions on compact convex sets, demonstrating potential for acceleration in specific models. AI

IMPACT Introduces novel sampling techniques that could improve the efficiency and applicability of machine learning models in constrained environments.

RANK_REASON Two academic papers published on arXiv detailing new algorithms for constrained sampling in machine learning.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

New algorithms tackle constrained sampling in machine learning · 2 sources tracked

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COVERAGE [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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