Diffusion differentiable resampling

Jennifer Rosina Andersson; Zheng Zhao

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arXiv · 2512.10401

Diffusion differentiable resampling

Jennifer Rosina Andersson·Zheng Zhao

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Abstract

This paper is concerned with differentiable resampling in the context of sequential Monte Carlo (e.g., particle filtering). Drawing on reparametrisation, we propose a new resampling method that is informative and instantly differentiable, based on a training-free diffusion model surrogate. We theoretically prove that our diffusion resampling method provides a consistent resampling distribution, and we show empirically that it outperforms the state-of-the-art differentiable resampling methods on multiple filtering and parameter estimation benchmarks. Finally, we show that it achieves competitive end-to-end performance when used in learning a complex dynamics-decoder model with high-dimensional image observations.

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BibTeX RIS

Jennifer Rosina Andersson, Zheng Zhao. 2025-12-11. Diffusion differentiable resampling. https://arxiv.org/abs/2512.10401

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