Tag: particle
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Finite-Particle Rates for Regularized Stein Variational Gradient Descent
Finite-Particle Rates for Regularized Stein Variational Gradient Descent arXiv:2602.05172v1 Announce Type: new Abstract: We derive finite-particle rates for the regularized Stein variational gradient descent (R-SVGD) algorithm introduced by He et al. (2024) that corrects the constant-order bias of the SVGD by applying a resolvent-type preconditioner to the kernelized Wasserstein gradient. For the resulting interacting $N$-particle…
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Variational Formulation of the Particle Flow Particle Filter
Variational Formulation of the Particle Flow Particle Filter arXiv:2505.04007v1 Announce Type: new Abstract: This paper provides a formulation of the particle flow particle filter from the perspective of variational inference. We show that the transient density used to derive the particle flow particle filter follows a time-scaled trajectory of the Fisher-Rao gradient flow in the…
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Accelerating Particle-based Energetic Variational Inference
Accelerating Particle-based Energetic Variational Inference arXiv:2504.03158v1 Announce Type: new Abstract: In this work, we propose a novel particle-based variational inference (ParVI) method that accelerates the EVI-Im. Inspired by energy quadratization (EQ) and operator splitting techniques for gradient flows, our approach efficiently drives particles towards the target distribution. Unlike EVI-Im, which employs the implicit Euler method…
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Optimal Particle-based Approximation of Discrete Distributions (OPAD)
Optimal Particle-based Approximation of Discrete Distributions (OPAD) arXiv:2412.00545v1 Announce Type: new Abstract: Particle-based methods include a variety of techniques, such as Markov Chain Monte Carlo (MCMC) and Sequential Monte Carlo (SMC), for approximating a probabilistic target distribution with a set of weighted particles. In this paper, we prove that for any set of particles, there…