一个统一的决策规则,用于普遍的分销之外的检测
IEEE transactions on pattern analysis and machine intelligence
|December 9, 2025
概括
这项研究引入了一种全新的泛化Benjaminini Hochberg (g-BH) 程序,用于泛化分布外 (OOD) 检测. g-BH程序为控制错误发现率 (FDR) 提供了理论上的保证,并且比传统方法表现出更高的性能.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 统计推理 统计推理
背景情况:
- 对可靠的人工智能系统来说,通用分布外 (OOD) 检测至关重要.
- 现有的OOD检测方法往往侧重于得分函数,忽视决策规则.
- 对于OOD检测,需要一个强大的决策规则和理论保证.
研究的目的:
- 提出一种新的决策规则,以理论上的保证来实现普遍的OOD检测.
- 解决OOD检测中现有的基于值的决策规则的局限性.
- 通过改进的OOD检测来提高AI应用程序的可靠性和安全性.
主要方法:
- 将OOD检测作为一个假设测试问题.
- 开发了一种新的广义的本杰明尼·霍赫伯格 (g-BH) 程序.
- 分析g-BH程序的理论特性,包括FDR控制和FPR边界.
- 在PFR分析中使用尾状通用高斯分布家族.
主要成果:
- 在理论上,g-BH程序控制了错误发现率 (FDR),而不假设p值独立.
- 对g-BH程序的预期虚假阳性率 (FPR) 推导的上下限.
- 证明了g-BH程序的FPR在概率上趋于零.
- 经验结果表明,g-BH程序的表现优于OOD检测基准的传统基于值的规则.
结论:
- 拟议的g-BH程序提供了一个理论上可靠和经验上有效的决策规则,用于一般化的OOD检测.
- 这种方法显著改进了现有方法,提供了强大的FDR控制和低FPR.
- 在安全关键的应用中,g-BH程序提高了人工智能系统的可靠性.
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