Differentiable Particle Filtering via Entropy-Regularized Optimal Transport
Abstract
Particle Filtering (PF) methods are an established class of procedures for performing inference in non-linear state-space models. Resampling is a key ingredient of PF, necessary to obtain low variance likelihood and states estimates. However, traditional resampling methods result in PF-based loss functions being non-differentiable with respect to model and PF parameters. In a variational inference context, resampling also yields high variance gradient estimates of the PF-based evidence lower bound. By leveraging optimal transport ideas, we introduce a principled differentiable particle filter and provide convergence results. We demonstrate this novel method on a variety of applications.
Cite
@article{arxiv.2102.07850,
title = {Differentiable Particle Filtering via Entropy-Regularized Optimal Transport},
author = {Adrien Corenflos and James Thornton and George Deligiannidis and Arnaud Doucet},
journal= {arXiv preprint arXiv:2102.07850},
year = {2021}
}
Comments
9 pages of content + 11 pages supplementary, accepted for oral at ICML 2021