GEMSS: A Variational Method for Discovering Multiple Sparse Solutions in Classification and Regression Problems
The paper introduces GEMSS, a variational method that utilizes a single mixture model with built-in repulsion to efficiently discover multiple distinct, sparse, and statistically plausible solutions for high-dimensional classification and regression problems, outperforming existing baselines in both synthetic benchmarks and real-world applications.