  Robust machine learning models with accurately calibrated uncertainties are
crucial for safety-critical applications. Probabilistic machine learning and
especially the Bayesian formalism provide a systematic framework to incorporate
robustness through the distributional estimates and reason about uncertainty.
Recent works have shown that approximate inference approaches that take the
weight space uncertainty of neural networks to generate ensemble prediction are
the state-of-the-art. However, architecture choices have mostly been ad hoc,
which essentially ignores the epistemic uncertainty from the architecture
space. To this end, we propose a Unified probabilistic architecture and weight
ensembling Neural Architecture Search (UraeNAS) that leverages advances in
probabilistic neural architecture search and approximate Bayesian inference to
generate ensembles form the joint distribution of neural network architectures
and weights. The proposed approach showed a significant improvement both with
in-distribution (0.86% in accuracy, 42% in ECE) CIFAR-10 and
out-of-distribution (2.43% in accuracy, 30% in ECE) CIFAR-10-C compared to the
baseline deterministic approach.
