AI RESEARCH
Multirate Stein Variational Gradient Descent for Efficient Bayesian Sampling
arXiv CS.LG
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ArXi:2604.03981v1 Announce Type: new Many particle-based Bayesian inference methods use a single global step size for all parts of the update. In Stein variational gradient descent (SVGD), however, each update combines two qualitatively different effects: attraction toward high-posterior regions and repulsion that preserves particle diversity. These effects can evolve at different rates, especially in high-dimensional, anisotropic, or hierarchical posteriors, so one step size can be unstable in some regions and inefficient in others.