GEMSS: A Variational Bayesian Method for Discovering Multiple Sparse Solutions in Classification and Regression Problems
This paper introduces GEMSS, a variational Bayesian algorithm that utilizes a structured spike-and-slab prior and a mixture of Gaussians to simultaneously discover multiple diverse sparse solutions in high-dimensional classification and regression problems, outperforming existing methods in both synthetic benchmarks and real-world applications.