Conformal Prediction

Calibrating AI Models for Few-Shot Demodulation via Conformal Prediction

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Artificial Intelligent (AI) tools can be useful to address model deficits in the design of communication systems. However, conventional learning-based AI algorithms yield poorly calibrated decisions, unabling to quantify their outputs uncertainty. While Bayesian learning can enhance calibration by capturing epistemic uncertainty caused by limited data availability, formal calibration guarantees only hold under strong assumptions about the ground-truth, unknown, data generation mechanism.

2023_05_05_slides_ICASSP2023_Calibrating_AI.pdf

Slides (124)

Categories:: Other applications of machine learning (MLR-APPL)
Communication and Sensing aspects of Sensor Networks, Wireless and Ad-Hoc Networks

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