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.

翻译：本文开发了新的符合性方法,以测试是否从与参考集相同的分布中抽取了新的观测结果。以创新的方式混合诱导性和感导性符合性推论,所述方法可以有原则地根据已知分配外数据从已知分配外数据得出的依赖性侧信息重新加权标准符合性方数值,并可以自动利用从任何收集的单级和二进制分类器中采集的最强大的模型。解决方案可以通过抽样分离或通过新的转导交叉校准+办法加以实施,由于比现有的交叉校准方法更加严格的保证,这种办法也可以用于其他符合性推论的应用。在对多个测试框架内的虚假发现率控制和权力进行了研究之后,通过模拟以及图像识别和表格式数据的应用,展示了拟议解决方案超越了标准的符合性方数值。