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New algorithm samples composite log-concave distributions efficiently

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 2 sources

Researchers have developed a new proximal gradient algorithm designed to sample from composite log-concave distributions. This algorithm assumes access to gradient evaluations for one part of the distribution and a restricted Gaussian oracle for the other. The proposed method achieves state-of-the-art iteration counts for sampling, matching previous results for simpler cases and extending to non-log-concave distributions and non-smooth functions. AI

Summary written by gemini-2.5-flash-lite from 2 sources. How we write summaries →

IMPACT Introduces a novel sampling technique that could improve efficiency in statistical modeling and machine learning applications.

RANK_REASON The cluster contains an academic paper detailing a new algorithm for statistical sampling.

Read on arXiv stat.ML →

COVERAGE [2]

  1. arXiv stat.ML TIER_1 · Linghai Liu, Sinho Chewi ·

    A proximal gradient algorithm for composite log-concave sampling

    arXiv:2605.12461v1 Announce Type: cross Abstract: We propose an algorithm to sample from composite log-concave distributions over $\mathbb{R}^d$, i.e., densities of the form $\pi\propto e^{-f-g}$, assuming access to gradient evaluations of $f$ and a restricted Gaussian oracle (RG…

  2. arXiv stat.ML TIER_1 · Sinho Chewi ·

    A proximal gradient algorithm for composite log-concave sampling

    We propose an algorithm to sample from composite log-concave distributions over $\mathbb{R}^d$, i.e., densities of the form $π\propto e^{-f-g}$, assuming access to gradient evaluations of $f$ and a restricted Gaussian oracle (RGO) for $g$. The latter requirement means that we can…

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