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New Paper Explores McDiarmid's Inequality via Approximate Tensorization of Entropy

By PulseAugur Editorial · [2 sources] ·

Researchers have published a paper detailing advancements in McDiarmid's inequality, a tool applicable to statistics, learning theory, and theoretical computer science. The work highlights how approximate tensorization of entropy (ATE) implies McDiarmid's inequality and derives a version for non-isotropic Gaussian random vectors. The findings also extend concentration inequalities to strongly log-concave and log-smooth probability measures, improving upon prior results for non-i.i.d. observations. AI

RANK_REASON The cluster contains a research paper published on arXiv detailing theoretical advancements in mathematical statistics and learning theory.

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New Paper Explores McDiarmid's Inequality via Approximate Tensorization of Entropy

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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Valentin Roth ·

    On McDiarmid's Inequality under Dependence via Approximate Tensorization of Entropy

    arXiv:2606.12720v1 Announce Type: cross Abstract: We argue that dependent versions of McDiarmid's inequality are a useful but underutilized tool in mathematical statistics, learning theory and theoretical computer science. To make this point, we first highlight that approximate t…

  2. arXiv stat.ML TIER_1 English(EN) · Valentin Roth ·

    On McDiarmid's Inequality under Dependence via Approximate Tensorization of Entropy

    We argue that dependent versions of McDiarmid's inequality are a useful but underutilized tool in mathematical statistics, learning theory and theoretical computer science. To make this point, we first highlight that approximate tensorization of entropy (ATE) implies McDiarmid's …

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