Differentiable Systematic Resampling for Variational Sequential Monte Carlo

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.

Machine Learning (stat.ML); Machine Learning (cs.LG); Signal Processing (eess.SP)

arXiv:2610.12094 [stat.ML]

(or arXiv:2610.12094v1 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2610.12094 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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