English(EN) Score-based Outlier Generation via Controlling the Radon-Nikodym Derivative

新方法通过控制Radon-Nikodym导数来生成异常值

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-14 04:00

研究人员开发了一种通过控制Radon-Nikodym导数来生成异常数据点的新方法，该方法明确管理低概率事件的幅度。该方法通过使用来自似然分布的项来缩放分数，从而修改了扩散分数函数，而无需重新训练模型。实验表明，该方法可以生成与底层数据几何形状一致的可控低概率样本。 AI

影响这项研究通过生成可控的低概率样本，为算法的压力测试提供了一种新颖的方法，有可能提高模型的鲁棒性。

排序理由该项目是一篇学术论文，详细介绍了一种用于机器学习中异常值生成的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Amartya Mukherjee, Tristan Milne, Kry Yik-Chau Lui, Stephanie Hazlewood, Jun Liu · 2026-09-14 04:00

基于得分的异常值生成与Radon-Nikodym导数控制

arXiv:2609.12113v1 Announce Type: new Abstract: Outliers are important for stress-testing algorithms and understanding system behaviour under rare conditions. Despite being commonly described as low-likelihood events, existing generative approaches rarely control likelihood expli…