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New research details tight sampling complexity for log-concave distributions

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method to analyze the complexity of sampling smooth, strongly log-concave distributions using stochastic gradient oracles. The study establishes a tight bound for sampling complexity, which is simultaneously adaptive to the condition number and accuracy parameters. This bound also simplifies to a logarithmic dependency on the condition number in the noiseless setting. AI

IMPACT Provides theoretical underpinnings for sampling methods potentially used in AI model training.

RANK_REASON The cluster contains an academic paper detailing a new theoretical finding in statistics. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New research details tight sampling complexity for log-concave distributions

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The cluster contains an academic paper detailing a new theoretical finding in statistics. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Weiming Ou, Xiao Wang ·

    Tight Sampling Complexity with stochastic gradient oracles in Fixed Dimensions

    arXiv:2609.12590v1 Announce Type: cross Abstract: We investigate the stochastic-gradient query complexity of sampling smooth strongly log-concave distributions in any fixed Euclidean dimension. The potential is $\mu$-strongly convex and $L$-smooth, with an unknown mode in the bal…

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