论文标题
具有标签异常值的强大分布测试的集成共形P值
Integrative conformal p-values for powerful out-of-distribution testing with labeled outliers
论文作者
论文摘要
本文开发了新型的保形方法，以测试是否从与参考集相同的分布中取样了新的观察结果。以创新的方式将感应性和跨传感性的共同点融合，所描述的方法可以以原则上的方式基于已知的脱离分布数据的依赖侧信息重新权重标准p值，并且可以自动利用任何一台级别和binary分类器的最强大模型的优势。该解决方案可以通过样品分裂或通过新颖的转置交叉验证+方案来实现，该方案与现有的交叉验证方法相比，由于更严格的保证，该方案也可能在其他共同推理的其他应用中有用。在研究错误的发现率控制和具有多个可能异常值的虚假发现率控制和功率之后，提出的解决方案显示出通过模拟以及用于图像识别和表格数据的应用来超过标准的共形p值。
This paper develops novel conformal methods to test whether a new observation was sampled from the same distribution as a reference set. Blending inductive and transductive conformal inference in an innovative way, the described methods can re-weight standard conformal p-values based on dependent side information from known out-of-distribution data in a principled way, and can automatically take advantage of the most powerful model from any collection of one-class and binary classifiers. The solution can be implemented either through sample splitting or via a novel transductive cross-validation+ scheme which may also be useful in other applications of conformal inference, due to tighter guarantees compared to existing cross-validation approaches. After studying false discovery rate control and power within a multiple testing framework with several possible outliers, the proposed solution is shown to outperform standard conformal p-values through simulations as well as applications to image recognition and tabular data.
