Robust Bayesian Learning for Reliable Wireless AI: Framework and Applications

TL;DR

This paper explores robust Bayesian learning to improve the reliability and robustness of machine learning models in wireless communication, addressing issues like calibration, outliers, and model misspecification.

Contribution

It introduces a framework of robust Bayesian learning tailored for wireless communication and demonstrates its advantages over traditional methods in various practical scenarios.

Findings

Enhanced accuracy in wireless tasks

Improved calibration of uncertainty estimates

Greater robustness to outliers and model misspecification

Abstract

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.