English(EN) When Metropolis and Hastings Meet Bradley and Terry: Exact MCMC From Preference Voting

新的 MCMC 采样器使用偏好投票进行条件采样

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-02 04:00

研究人员开发了 Pref-MH，一种新颖的马尔可夫链蒙特卡洛 (MCMC) 采样器，它能够从由语义属性定义的分布中进行精确的条件采样，即使在无法进行精确密度评估的情况下也是如此。该方法利用成对比较，类似于 Bradley-Terry 模型，来推断偏好赔率并计算 Metropolis-Hastings 比率。Pref-MH 被证明在其类别中是最优的，并已成功应用于文本生成、分子设计以及使用大型语言模型和视觉-语言模型作为裁判的图像生成等任务。 AI

影响通过利用可访问的比较反馈，能够为生成模型提供更灵活的条件采样。

排序理由该集群包含一篇详细介绍新算法方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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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报道来源 [1]

arXiv stat.ML TIER_1 English(EN) · Ariel Smogorghevski, Nir Rosenfeld, Yaniv Romano · 2026-09-02 04:00

当 Metropolis 和 Hastings 遇上 Bradley 和 Terry：来自偏好投票的精确 MCMC

arXiv:2609.00905v1 Announce Type: cross Abstract: Sampling from distributions conditioned on desired semantic properties is an emerging challenge in modern generative modeling. Metropolis-Hastings (MH) provides a principled route to conditional sampling, but requires access to ex…

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当 Metropolis 和 Hastings 遇上 Bradley 和 Terry：来自偏好投票的精确 MCMC

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