We explore the use of proper priors for variance parameters of certain sparse Bayesian
regression models. This leads to a connection between sparse Bayesian learning (SBL) …

This paper presents a general, fully Bayesian framework for sparse supervised-learning
problems based on the horseshoe prior. The horseshoe prior is a member of the family of …

In the context of statistical machine learning, sparse learning is a procedure that seeks a
reconciliation between two competing aspects of a statistical model: good predictive power …

In this thesis we investigate centralized and distributed variants of sparse Bayesian learning
(SBL), an effective probabilistic regression method used in machine learning. Since …

Sparse Bayesian learning suffers from impractical, overconfident predictions where the
uncertainty tends to be maximal around the observations. We propose an alternative …

Modern statistical learning algorithms are capable of amazing flexibility, but struggle with
interpretability. One possible solution is sparsity: making inference such that many of the …

Popular convex approaches for sparse estimation such as Lasso and Multiple Kernel
Learning (MKL) can be derived in a Bayesian setting, starting from a particular stochastic …

We address the problem of learning a sparse Bayesian network structure for continuous
variables in a high-dimensional space. The constraint that the estimated Bayesian network …

Sparse Bayesian Learning (SBL) models are extensively used in signal processing and
machine learning for promoting sparsity through hierarchical priors. The hyperparameters in …

While Bayesian analogues of lasso regression have become popular, comparatively little
has been said about formal treatments of model uncertainty in such settings. This paper …