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arXiv · 2211.04847

Hyper-Parameter Auto-Tuning for Sparse Bayesian Learning

Dawei Gao·Qinghua Guo·Ming Jin·Guisheng Liao·Yonina C. Eldar
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Abstract

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.

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Dawei Gao, Qinghua Guo, Ming Jin, Guisheng Liao, Yonina C. Eldar. 2022-11-09. Hyper-Parameter Auto-Tuning for Sparse Bayesian Learning. https://arxiv.org/abs/2211.04847

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