Riesz-Kernel Stein Variational Gradient Descent: Renormalized Entropy and Long-Time Particle Limits
Not on the map. Why?
Abstract (arXiv)
Stein variational gradient descent (SVGD) transports interacting particles toward a target distribution through deterministic kernelized dynamics. Singular Riesz kernels are attractive because they can provide quantitative population-level convergence, but at the finite-particle level the corresponding Stein energy has infinite self-interaction. We study periodic Riesz SVGD with self-interaction removed and prove a many-particle, long-time sampling theorem. Throughout the range in which the singular Stein energy is locally integrable, under a uniform bound on the initial relative entropy per particle, the time-averaged empirical-measure law converges weakly to the point mass (δπ) at the target as the particle number and any diverging averaging horizon tend to infinity. We also show that the empirical-measure laws induced by invariant particle laws of finite relative entropy converge weakly to (δπ), without a uniform entropy bound. Below the logarithmic singularity threshold, we obtain an explicit algebraic finite-particle error bound. These results extend the joint-entropy approach for smooth-kernel SVGD to singular interactions.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 22 references that are in the map. First posted on arXiv 16 Jul 2026.
Builds on
15 of 22 references.
References · 15
- 2016Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithmcited by 498
- 2017Stein Variational Gradient Descent as Gradient Flowcited by 149
- 2016A Kernel Test of Goodness of Fitcited by 143
- 2017Measuring Sample Quality with Kernelscited by 119
- 2019On the geometry of Stein variational gradient descentcited by 82
- 2020SVGD as a kernelized Wasserstein gradient flow of the chi-squared divergencecited by 64
- 2020A Non-Asymptotic Analysis for Stein Variational Gradient Descentcited by 61
- 2018Scaling limit of the Stein variational gradient descent: the mean field regimecited by 35
- 2019Stein Variational Gradient Descent With Matrix-Valued Kernelscited by 32
- 2022A Finite-Particle Convergence Rate for Stein Variational Gradient Descentcited by 22
- 2021Stein Variational Gradient Descent: many-particle and long-time asymptoticscited by 15
- 2024Improved Finite-Particle Convergence Rates for Stein Variational Gradient Descentcited by 13
Built on by
Not cited yet.