A Non-Asymptotic Analysis for Stein Variational Gradient Descent

Part of Advances in Neural Information Processing Systems 33 (NeurIPS 2020)

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Anna Korba, Adil Salim, Michael Arbel, Giulia Luise, Arthur Gretton


We study the Stein Variational Gradient Descent (SVGD) algorithm, which optimises a set of particles to approximate a target probability distribution $\pi\propto e^{-V}$ on $\R^d$. In the population limit, SVGD performs gradient descent in the space of probability distributions on the KL divergence with respect to $\pi$, where the gradient is smoothed through a kernel integral operator. In this paper, we provide a novel finite time analysis for the SVGD algorithm. We provide a descent lemma establishing that the algorithm decreases the objective at each iteration, and rates of convergence. We also provide a convergence result of the finite particle system corresponding to the practical implementation of SVGD to its population version.