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New research details information complexity in broadcast model coin problem

By PulseAugur Editorial · [1 sources] ·

A new research paper published on arXiv explores the coin problem within the broadcast model, focusing on distributed testing of specific probability distributions. The study characterizes information complexity and identifies optimal protocols based on different channel types, including a novel mixed Hellinger--Jensen--Shannon inequality. The findings have applications in deriving lower bounds for multi-pass streaming settings and testing arbitrary discrete distributions. AI

IMPACT This theoretical research in information complexity may inform future advancements in distributed AI systems and data analysis.

RANK_REASON The cluster contains a single academic paper published on arXiv detailing theoretical computer science research. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv stat.ML →

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

New research details information complexity in broadcast model coin problem

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The cluster contains a single academic paper published on arXiv detailing theoretical computer science research. [lever_c_demoted from research: ic=1 ai=0.4]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Hadi Kazemi, Varun Jog ·

    Tight Information Complexity of the Coin Problem in the Broadcast Model

    arXiv:2608.02776v1 Announce Type: cross Abstract: We study distributed testing of $\mathrm{Ber}(\alpha)$ versus $\mathrm{Ber}(\beta)$ in the broadcast, or shared-blackboard, model. For protocols with constant advantage, we characterise up to universal constant factors the informa…

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