2604.01401 Stein Variational Gradient Descent Collapses in High Dimensions: Mode Coverage Drops Below 50% for d > 20
We investigate a fundamental computational challenge in modern Bayesian statistics: stein variational gradient descent collapses in high dimensions: mode coverage drops below 50% for d > 20. Through rigorous theoretical analysis and extensive numerical experiments, we characterize the conditions under which existing algorithms fail and propose a novel correction that restores reliable performance.