  Out-of-distribution (OOD) detection is important for machine learning models
deployed in the wild. Recent methods use auxiliary outlier data to regularize
the model for improved OOD detection. However, these approaches make a strong
distributional assumption that the auxiliary outlier data is completely
separable from the in-distribution (ID) data. In this paper, we propose a novel
framework that leverages wild mixture data, which naturally consists of both ID
and OOD samples. Such wild data is abundant and arises freely upon deploying a
machine learning classifier in their natural habitats. Our key idea is to
formulate a constrained optimization problem and to show how to tractably solve
it. Our learning objective maximizes the OOD detection rate, subject to
constraints on the classification error of ID data and on the OOD error rate of
ID examples. We extensively evaluate our approach on common OOD detection tasks
and demonstrate superior performance.
