{"ID":2828917,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.22153","arxiv_id":"2512.22153","title":"Sampling with Shielded Langevin Monte Carlo Using Navigation Potentials","abstract":"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.","short_abstract":"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 nove...","url_abs":"https://arxiv.org/abs/2512.22153","url_pdf":"https://arxiv.org/pdf/2512.22153v1","authors":"[\"Nicolas Zilberstein\",\"Santiago Segarra\",\"Luiz Chamon\"]","published":"2025-12-15T11:39:00Z","proceeding":"stat.CO","tasks":"[\"stat.CO\",\"cs.LG\",\"stat.ML\"]","methods":"[]","has_code":false}