  Machine learning (ML) systems often encounter Out-of-Distribution (OoD)
errors when dealing with testing data coming from a distribution different from
training data. It becomes important for ML systems in critical applications to
accurately quantify its predictive uncertainty and screen out these anomalous
inputs. However, existing OoD detection approaches are prone to errors and even
sometimes assign higher likelihoods to OoD samples. Unlike standard learning
tasks, there is currently no well established guiding principle for designing
OoD detection architectures that can accurately quantify uncertainty. To
address these problems, we first seek to identify guiding principles for
designing uncertainty-aware architectures, by proposing Neural Architecture
Distribution Search (NADS). NADS searches for a distribution of architectures
that perform well on a given task, allowing us to identify common building
blocks among all uncertainty-aware architectures. With this formulation, we are
able to optimize a stochastic OoD detection objective and construct an ensemble
of models to perform OoD detection. We perform multiple OoD detection
experiments and observe that our NADS performs favorably, with up to 57%
improvement in accuracy compared to state-of-the-art methods among 15 different
testing configurations.
