Sampling with Shielded Langevin Monte Carlo Using Navigation Potentials
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866912790796566528 |
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| author | Zilberstein, Nicolas Segarra, Santiago Chamon, Luiz |
| author_facet | Zilberstein, Nicolas Segarra, Santiago Chamon, Luiz |
| contents | We introduce shielded Langevin Monte Carlo (LMC), a constrained sampler inspired by navigation functions, capable of sampling from unnormalized target distributions defined over punctured supports. In other words, this approach samples from non-convex spaces defined as convex sets with convex holes. This defines a novel and challenging problem in constrained sampling. To do so, the sampler incorporates a combination of a spatially adaptive temperature and a repulsive drift to ensure that samples remain within the feasible region. Experiments on a 2D Gaussian mixture and multiple-input multiple-output (MIMO) symbol detection showcase the advantages of the proposed shielded LMC in contrast to unconstrained cases. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_22153 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sampling with Shielded Langevin Monte Carlo Using Navigation Potentials Zilberstein, Nicolas Segarra, Santiago Chamon, Luiz Computation Machine Learning We introduce shielded Langevin Monte Carlo (LMC), a constrained sampler inspired by navigation functions, capable of sampling from unnormalized target distributions defined over punctured supports. In other words, this approach samples from non-convex spaces defined as convex sets with convex holes. This defines a novel and challenging problem in constrained sampling. To do so, the sampler incorporates a combination of a spatially adaptive temperature and a repulsive drift to ensure that samples remain within the feasible region. Experiments on a 2D Gaussian mixture and multiple-input multiple-output (MIMO) symbol detection showcase the advantages of the proposed shielded LMC in contrast to unconstrained cases. |
| title | Sampling with Shielded Langevin Monte Carlo Using Navigation Potentials |
| topic | Computation Machine Learning |
| url | https://arxiv.org/abs/2512.22153 |