Poster
in
Workshop: OPT 2022: Optimization for Machine Learning
A Finite-Particle Convergence Rate for Stein Variational Gradient Descent
Jiaxin Shi · Lester Mackey
Abstract:
We provide a first finite-particle convergence rate for Stein variational gradient descent (SVGD). Specifically, with certain choices of step size sequences, SVGD with n particles drive the kernel Stein discrepancy to zero at the rate O(1√loglogn)O(1√loglogn). We suspect that the dependence on n can be improved, and we hope that our explicit, non-asymptotic proof strategy will serve as a template for future refinements.
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