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New sampling methods improve efficiency for complex distributions · 2 sources tracked

By PulseAugur Editorial · [5 sources] ·

Researchers have developed a new method called Gradient-free Riemannian Langevin Sampler (GRiLS) to improve the efficiency of sampling multimodal probability distributions. This approach aims to overcome limitations in standard Markov Chain Monte Carlo methods that can lead to poor mixing and mode trapping. GRiLS utilizes a Riemannian metric to reshape the local geometry, facilitating transitions between modes without requiring gradient evaluations of the target density, making it suitable for complex computational targets. Additionally, a separate paper explores fast determinantal sampling on general spaces, offering kernel-based alternatives for dataset representations and establishing rate guarantees for sampling on Riemannian manifolds and networks. AI

IMPACT These new sampling techniques could enhance the efficiency and accuracy of machine learning models, particularly in complex scenarios where traditional methods struggle.

RANK_REASON The cluster contains two arXiv papers detailing new research in sampling methods for probability distributions and datasets.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 5 sources. How we write summaries →

New sampling methods improve efficiency for complex distributions · 2 sources tracked

COVERAGE [5]

  1. arXiv cs.LG TIER_1 Nederlands(NL) · Ricardo Baptista, Olivier Zahm ·

    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 ·

    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) ·

    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 ·

    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 ·

    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…

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