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New Bounds Set for Rényi and Min-Entropy Estimation

By PulseAugur Editorial · [1 sources] ·

Researchers have established new sample complexity bounds for estimating Rényi and min-entropy, which are fundamental concepts in information theory and property testing. The study provides precise characterizations for the number of samples required to accurately estimate these entropy measures for a k-symbol alphabet. Notably, min-entropy estimation requires significantly more samples than Shannon entropy, with a sample complexity of \Theta(k \log k), correcting previous assumptions. AI

IMPACT Establishes theoretical foundations for information estimation, potentially impacting future AI model evaluation and data analysis techniques.

RANK_REASON The cluster contains an academic paper detailing theoretical research findings. [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 Bounds Set for Rényi and Min-Entropy Estimation

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The cluster contains an academic paper detailing theoretical research findings. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Arman Adibi, Piotr Krysta ·

    Tight Sample Bounds for Renyi and Min-Entropy Estimation

    arXiv:2607.16966v1 Announce Type: cross Abstract: Estimating entropy from samples is fundamental in information theory and property testing. Shannon entropy measures average uncertainty and can be estimated to constant additive accuracy over a $k$-symbol alphabet using $\Theta(k/…

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