Choosing the values of hyper-parameters in sparse Bayesian learning (SBL) can significantly impact performance. However, the hyper-parameters are normally tuned manually, which is often a difficult task. Most recently, effective automatic hyper-parameter tuning was achieved by using an empirical auto-tuner. In this work, we address the issue of hyper-parameter auto-tuning using neural network (NN)-based learning. Inspired by the empirical auto-tuner, we design and learn a NN-based auto-tuner, and show that considerable improvement in convergence rate and recovery performance can be achieved.
Cite
@article{arxiv.2211.04847,
title = {Hyper-Parameter Auto-Tuning for Sparse Bayesian Learning},
author = {Dawei Gao and Qinghua Guo and Ming Jin and Guisheng Liao and Yonina C. Eldar},
journal= {arXiv preprint arXiv:2211.04847},
year = {2022}
}