Differentiable Systematic Resampling for Variational Sequential Monte Carlo

Fredrik Cumlin; Saikat Chatterjee

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arXiv · 2610.12094

Differentiable Systematic Resampling for Variational Sequential Monte Carlo

Fredrik Cumlin·Saikat Chatterjee

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Abstract

Particle filters are a standard tool for nonlinear state estimation, but their resampling step is discrete, preventing gradient-based learning in variational sequential Monte Carlo. We introduce Differentiable Systematic Resampling (DSR), a temperature-controlled relaxation of systematic resampling, that preserves the CDF-ordered, banded structure of systematic resampling while enabling full gradient flow. DSR converges to exact systematic resampling as the temperature vanishes, and we prove a pointwise exponential convergence rate for the induced bias. Compared to optimal-transport-based differentiable resampling, DSR avoids iterative solvers and has substantially lower computational overhead. Experiments on stochastic dynamical systems and real-world handwriting data show that DSR achieves comparable or superior filtering and dynamics learning performance.

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BibTeX RIS

Fredrik Cumlin, Saikat Chatterjee. 2026-10-08. Differentiable Systematic Resampling for Variational Sequential Monte Carlo. https://arxiv.org/abs/2610.12094

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