Efficient feature selection from high-dimensional datasets is a very important challenge in
many data-driven fields of science and engineering. We introduce a statistical mechanics …

Rapid and ongoing technology developments enable researchers to collect large scale,
high-dimensional data in a wide range of areas in science and technology. The availability …

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

Sparse Bayesian Learning (SBL) constructs an extremely sparse probabilistic model with
very competitive generalization. However, SBL needs to invert a big covariance matrix with …

Recently, there has been a significant focus on exploring the theoretical aspects of deep
learning, especially regarding its performance in classification tasks. Bayesian deep …

Naive Bayes (NB) is well-known to be a simple but effective classifier, especially when
combined with feature selection. Unfortunately, feature selection methods are often greedy …

We consider the application of Bayesian spike-andslab models in high-dimensional feature
selection problems. To do so, we propose a simple yet effective fast approximate Bayesian …

Due to its linear complexity, naive Bayes classification remains an attractive supervised
learning method, especially in very large-scale settings. We propose a sparse version of …

Learning decompositions of expensive-to-evaluate black-box functions promises to scale
Bayesian optimisation (BO) to high-dimensional problems. However, the success of these …

While sparse Bayesian learning (SBL) has attracted a great amount of attention in a wide
range of areas, its practical utility is limited due to the high computational cost. To address …