@inproceedings{60b1745dd4124724b8fe4ee658d19136,
title = "Uncertainty Quantification For Learned ISTA",
abstract = "Model-based deep learning solutions to inverse problems have attracted increasing attention in recent years as they bridge state-of-the-art numerical performance with interpretability. In addition, the incorporated prior domain knowledge can make the training more efficient as the smaller number of parameters allows the training step to be executed with smaller datasets. Algorithm unrolling schemes stand out among these model-based learning techniques. Despite their rapid advancement and their close connection to traditional high-dimensional statistical methods, they lack certainty estimates and a theory for uncertainty quantification is still elusive. This work provides a step towards closing this gap proposing a rigorous way to obtain confidence intervals for the LISTA estimator.",
keywords = "Compressive Sensing, Interpretability, Neural Networks, Uncertainty Quantification, Unrolling",
author = "Frederik Hoppe and Verdun, {Claudio Mayrink} and Hannah Laus and Felix Krahmer and Holger Rauhut",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 33rd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2023 ; Conference date: 17-09-2023 Through 20-09-2023",
year = "2023",
doi = "10.1109/MLSP55844.2023.10285912",
language = "English",
series = "IEEE International Workshop on Machine Learning for Signal Processing, MLSP",
publisher = "IEEE Computer Society",
editor = "Danilo Comminiello and Michele Scarpiniti",
booktitle = "Proceedings of the 2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing, MLSP 2023",
}