Sparse Bayesian Learning

Sparse Bayesian learning (SBL) is a statistical framework aiming to efficiently estimate sparse solutions from high-dimensional data by incorporating prior knowledge about the sparsity of the underlying model. Current research focuses on improving the robustness and efficiency of SBL algorithms, including developing novel priors (e.g., diversified block sparse priors), refining hyperparameter estimation techniques (e.g., using expectation-maximization or neural network-based auto-tuning), and adapting SBL to various applications through model architectures like rational polynomial chaos expansions. These advancements enhance SBL's applicability in diverse fields such as signal processing, fault diagnosis, and brain-computer interfaces, offering improved accuracy and reduced computational cost compared to traditional methods.

Papers

November 14, 2024

Sparse Bayesian Generative Modeling for Compressive Sensing
Benedikt Böck, Sadaf Syed, Wolfgang Utschick
Variational Inference Compressed Sensing Linear Inverse Problem Sparse Bayesian Learning Compressed Communication

August 7, 2024

Maximum a Posteriori Estimation for Linear Structural Dynamics Models Using Bayesian Optimization with Rational Polynomial Chaos Expansions
Felix Schneider, Iason Papaioannou, Bruno Sudret, Gerhard Müller
Bayesian Optimization Bayesian Perspective Structural Dynamic Polynomial Chaos Expansion Maximum Mean Posteriori Error Sparse Bayesian Learning Sparse Bayesian

April 1, 2024

Sparse Bayesian Correntropy Learning for Robust Muscle Activity Reconstruction from Noisy Brain Recordings
Yuanhao Li, Badong Chen, Natsue Yoshimura, Yasuharu Koike, Okito Yamashita
Neural Recording Sparse Bayesian Learning Sparse Bayesian Correntropy Learning

February 7, 2024

Block Sparse Bayesian Learning: A Diversified Scheme
Yanhao Zhang, Zhihan Zhu, Yong Xia
LeArning Abstract Sparse Signal Expert Diversification Block Sparsity Sparse Bayesian Learning

January 4, 2024

Hyperparameter Estimation for Sparse Bayesian Learning Models
Feng Yu, Lixin Shen, Guohui Song
Sparsity Increase Sparse Bayesian Learning Hierarchical Prior Linear Surrogate

October 20, 2023

A Sparse Bayesian Learning for Diagnosis of Nonstationary and Spatially Correlated Faults with Application to Multistation Assembly Systems
Jihoon Chung, Zhenyu Kong
Medical Diagnosis Robotic Assembly Fault Diagnosis Post Fault Trajectory Variational Bayesian Inference Fault Diagnosis Method Sparse Bayesian Learning

February 28, 2023

Sequential edge detection using joint hierarchical Bayesian learning
Yao Xiao, Anne Gelb, Guohui Song
Sparsity Increase Edge Detection Image Level Sparse Bayesian Learning

November 9, 2022

Hyper-Parameter Auto-Tuning for Sparse Bayesian Learning
Dawei Gao, Qinghua Guo, Ming Jin, Guisheng Liao, Yonina C. Eldar
Hyper Parameter Tuning Sparse Bayesian Learning

October 28, 2022

A Novel Sparse Bayesian Learning and Its Application to Fault Diagnosis for Multistation Assembly Systems
Jihoon Chung, Bo Shen, Zhenyu, Kong
Application Proficiency Robotic Assembly Posterior Distribution Variational Bayesian Inference Sparse Bayesian Learning

June 6, 2022

Sparse Bayesian Learning for Complex-Valued Rational Approximations
Felix Schneider, Iason Papaioannou, Gerhard Müller
Polynomial Chaos Expansion Sparse Bayesian Learning

February 25, 2022

High-Dimensional Sparse Bayesian Learning without Covariance Matrices
Alexander Lin, Andrew H. Song, Berkin Bilgic, Demba Ba
Covariance Matrix Posterior Approximation Fisher Weighted Sparse Bayesian Learning

January 20, 2022

DDPG-Driven Deep-Unfolding with Adaptive Depth for Channel Estimation with Sparse Bayesian Learning
Qiyu Hu, Shuhan Shi, Yunlong Cai, Guanding Yu
Stochastic Gradient Descent Channel Estimation Deep Unfolding Sparse Bayesian Learning Adaptive Depth