mlsanityData to 14 Oct 2026

Robust Bayesian Learning for Reliable Wireless AI: Framework and Applications

arXiv 2207.00300 ↗First posted 1 Jul 2022cs.LG cs.AI cs.ITcited by 9

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Abstract (arXiv)

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.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 33 of its 83 references that are in the map. First posted on arXiv 1 Jul 2022.

Builds on

33 of 83 references.

References · 33

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21 more
  1. 2020Robustness of Bayesian Neural Networks to Gradient-Based Attackscited by 48
  2. 2018Deepcode: Feedback Codes via Deep Learningcited by 42
  3. 2018A Very Brief Introduction to Machine Learning With Applications to Communication Systemscited by 35
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 6; 2026: 2.

9 papers.

  1. 2025How to Bridge the Sim-to-Real Gap in Digital Twin-Aided Telecommunication Networkscited by 6
  2. 2026Prediction-Powered Risk Monitoring of Deployed Models for Detecting Harmful Distribution Shiftscited by 3
  3. 2025Multi-Fidelity Hybrid Reinforcement Learning via Information Gain Maximizationcited by 2
  4. 2025To Trust or Not to Trust: On Calibration in ML-based Resource Allocation for Wireless Networkscited by 1
  5. 2025ATLAS: AI-Native Receiver Test-and-Measurement by Leveraging AI-Guided Searchcited by 1
  6. 2025Optimizing In-Context Learning for Efficient Full Conformal Predictioncited by 1
  7. 2022Calibrating AI Models for Wireless Communications via Conformal Predictioncited by 0
  8. 2025Characterizing 5G User Throughput via Uncertainty Modeling and Crowdsourced Measurementscited by 0
  9. 2026Optimizing Reinforcement Learning Training over Digital Twin Enabled Multi-fidelity Networkscited by 0