TY - JOUR
T1 - Robust Bayesian Learning for Reliable Wireless AI: Framework and Applications
AU - Zecchin, Matteo
AU - Park, Sangwoo
AU - Simeone, Osvaldo
AU - Kountouris, Marios
AU - Gesbert, David
N1 - Funding Information:
The work of Matteo Zecchin and David Gesbert is funded by the Marie Curie action WINDMILL (grant No. 813999), while Osvaldo Simeone and Sangwoo Park have received funding from the European Research Council (ERC) under the European Union's Horizon 2020 Research and Innovation Programme (Grant Agreement No. 725731). Marios Kountouris has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 Research and Innovation Programme (Grant agreement No. 101003431).
Publisher Copyright:
© 2015 IEEE.
PY - 2023/8/1
Y1 - 2023/8/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85151511077&partnerID=8YFLogxK
U2 - 10.1109/TCCN.2023.3261300
DO - 10.1109/TCCN.2023.3261300
M3 - Article
SN - 2332-7731
VL - 9
SP - 897
EP - 912
JO - IEEE Transactions on Cognitive Communications and Networking
JF - IEEE Transactions on Cognitive Communications and Networking
IS - 4
ER -