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New complexity bound simplifies logconcave distribution sampling

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new, unified complexity bound for sampling logconcave distributions. This bound is nearly tight and applies to various settings, including constrained and well-conditioned densities. The analysis introduces an improved bound for the Poincaré constant of a lifted distribution, leading to more efficient convergence rates. AI

RANK_REASON The cluster contains an academic paper detailing a new theoretical bound for sampling logconcave distributions.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New complexity bound simplifies logconcave distribution sampling

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The cluster contains an academic paper detailing a new theoretical bound for sampling logconcave distributions.
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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Yunbum Kook, Santosh S. Vempala ·

    A unified complexity bound for logconcave sampling

    arXiv:2606.12694v1 Announce Type: cross Abstract: We give a simple, unified, and nearly tight bound for sampling arbitrary logconcave distributions from a warm start using the In-and-Out algorithm along with exponential lifting. The main new ingredient in the analysis is an impro…

  2. arXiv stat.ML TIER_1 English(EN) · Santosh S. Vempala ·

    A unified complexity bound for logconcave sampling

    We give a simple, unified, and nearly tight bound for sampling arbitrary logconcave distributions from a warm start using the In-and-Out algorithm along with exponential lifting. The main new ingredient in the analysis is an improved bound on the Poincaré constant of a lifted dis…

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