# Hyper-Parameter Auto-Tuning for Sparse Bayesian Learning

arXiv: https://arxiv.org/abs/2211.04847 · HTML: https://mlsanity.com/paper/2211.04847/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 13 references that are in the map.
- First posted on arXiv 9 Nov 2022.

## Builds on (most cited first, 2 of 2)

- [Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing](https://mlsanity.com/paper/1912.10557/) (2019)
- [Unitary Approximate Message Passing for Sparse Bayesian Learning](https://mlsanity.com/paper/2101.09954/) (2021)

## Abstract (arXiv)

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.