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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2509.19816 |
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| _version_ | 1866908556585861120 |
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| author | Zeng, Zhijun Gan, Weiye Chen, Junqing Shi, Zuoqiang |
| author_facet | Zeng, Zhijun Gan, Weiye Chen, Junqing Shi, Zuoqiang |
| contents | In many engineering and applied science domains, high-dimensional nonlinear filtering is still a challenging problem. Recent advances in score-based diffusion models offer a promising alternative for posterior sampling but require repeated retraining to track evolving priors, which is impractical in high dimensions. In this work, we propose the Conditional Score-based Filter (CSF), a novel algorithm that leverages a set-transformer encoder and a conditional diffusion model to achieve efficient and accurate posterior sampling without retraining. By decoupling prior modeling and posterior sampling into offline and online stages, CSF enables scalable score-based filtering across diverse nonlinear systems. Extensive experiments on benchmark problems show that CSF achieves superior accuracy, robustness, and efficiency across diverse nonlinear filtering scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_19816 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Efficient Conditional Score-based Filter for High Dimensional Nonlinear Filtering Problems Zeng, Zhijun Gan, Weiye Chen, Junqing Shi, Zuoqiang Machine Learning 93E11, 62F15, 68T07 In many engineering and applied science domains, high-dimensional nonlinear filtering is still a challenging problem. Recent advances in score-based diffusion models offer a promising alternative for posterior sampling but require repeated retraining to track evolving priors, which is impractical in high dimensions. In this work, we propose the Conditional Score-based Filter (CSF), a novel algorithm that leverages a set-transformer encoder and a conditional diffusion model to achieve efficient and accurate posterior sampling without retraining. By decoupling prior modeling and posterior sampling into offline and online stages, CSF enables scalable score-based filtering across diverse nonlinear systems. Extensive experiments on benchmark problems show that CSF achieves superior accuracy, robustness, and efficiency across diverse nonlinear filtering scenarios. |
| title | An Efficient Conditional Score-based Filter for High Dimensional Nonlinear Filtering Problems |
| topic | Machine Learning 93E11, 62F15, 68T07 |
| url | https://arxiv.org/abs/2509.19816 |