Diffusion Path Samplers via Sequential Monte Carlo

• URL: http://arxiv.org/abs/2601.21951v1
• Date: Thu, 29 Jan 2026 16:32:12 GMT
• Title: Diffusion Path Samplers via Sequential Monte Carlo
• Authors: James Matthew Young, Paula Cordero-Encinar, Sebastian Reich, Andrew Duncan, O. Deniz Akyildiz,
• Abstract summary: We develop a diffusion-based sampler for target distributions known up to a normalising constant.<n>Our approach is based on a practical implementation of diffusion-annealed Langevin Monte Carlo.<n>We provide theoretical guarantees and empirically demonstrate the effectiveness of our method on several synthetic and real-world datasets.
• Score: 0.31690235522182103
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We develop a diffusion-based sampler for target distributions known up to a normalising constant. To this end, we rely on the well-known diffusion path that smoothly interpolates between a (simple) base distribution and the target distribution, widely used in diffusion models. Our approach is based on a practical implementation of diffusion-annealed Langevin Monte Carlo, which approximates the diffusion path with convergence guarantees. We tackle the score estimation problem by developing an efficient sequential Monte Carlo sampler that evolves auxiliary variables from conditional distributions along the path, which provides principled score estimates for time-varying distributions. We further develop novel control variate schedules that minimise the variance of these score estimates. Finally, we provide theoretical guarantees and empirically demonstrate the effectiveness of our method on several synthetic and real-world datasets.

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