新的采样方法提高了复杂分布的效率 · 跟踪 2 个来源

报道来源 [5]

1. arXiv cs.LG TIER_1 Nederlands(NL) · Ricardo Baptista, Olivier Zahm · 2026-07-09 04:00

   Gradient-free Riemannian Langevin Sampler

   arXiv:2607.07519v1 Announce Type: new Abstract: We address the problem of efficiently sampling multimodal probability distributions, where standard Markov Chain Monte Carlo methods often suffer from poor mixing and mode trapping. To mitigate these issues, we propose Gradient-free…

2. arXiv cs.LG TIER_1 Nederlands(NL) · Olivier Zahm · 2026-07-08 15:16

   Gradient-free Riemannian Langevin Sampler

   We address the problem of efficiently sampling multimodal probability distributions, where standard Markov Chain Monte Carlo methods often suffer from poor mixing and mode trapping. To mitigate these issues, we propose Gradient-free Riemannian Langevin Sampler (GRiLS), a novel pr…

3. Hugging Face Daily Papers TIER_1 Nederlands(NL) · 2026-07-08 15:16

   Gradient-free Riemannian Langevin Sampler

   We address the problem of efficiently sampling multimodal probability distributions, where standard Markov Chain Monte Carlo methods often suffer from poor mixing and mode trapping. To mitigate these issues, we propose Gradient-free Riemannian Langevin Sampler (GRiLS), a novel pr…

4. arXiv stat.ML TIER_1 English(EN) · Hoang-Son Tran, Pranav Gupta, Subhroshekhar Ghosh · 2026-07-09 04:00

   Fast determinantal sampling on general spaces and diffusion geometry

   arXiv:2607.06644v1 Announce Type: new Abstract: Determinantal point processes have recently emerged as a kernel-based alternative to standard independent sampling for constructing efficient minibatches, coresets, and other compact representations of large-scale datasets. In parti…

5. arXiv stat.ML TIER_1 English(EN) · Subhroshekhar Ghosh · 2026-07-07 15:26

   Fast determinantal sampling on general spaces and diffusion geometry

   Determinantal point processes have recently emerged as a kernel-based alternative to standard independent sampling for constructing efficient minibatches, coresets, and other compact representations of large-scale datasets. In particular, sampling mechanisms based on DPPs are bel…