  This work takes a critical look at the application of conventional machine
learning methods to wireless communication problems through the lens of
reliability and robustness. Deep learning techniques adopt a frequentist
framework, and are known to provide poorly calibrated decisions that do not
reproduce the true uncertainty caused by limitations in the size of the
training data. Bayesian learning, while in principle capable of addressing this
shortcoming, is in practice impaired by model misspecification and by the
presence of outliers. Both problems are pervasive in wireless communication
settings, in which the capacity of machine learning models is subject to
resource constraints and training data is affected by noise and interference.
In this context, we explore the application of the framework of robust Bayesian
learning. After a tutorial-style introduction to robust Bayesian learning, we
showcase the merits of robust Bayesian learning on several important wireless
communication problems in terms of accuracy, calibration, and robustness to
outliers and misspecification.
