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New bounds improve discrete probability distribution estimation

By PulseAugur Editorial · [2 sources] ·

Researchers have published new bounds for estimating discrete probability distributions using the $\ell_\infty$ norm. The work provides minimax bounds in expectation and high-probability tail bounds. This research resolves open questions from Kontorovich and Painsky (JMLR, 2025), including an empirical version of their tightest risk bound and the identification of the worst-case extremal distribution. AI

IMPACT Advances theoretical understanding in statistical machine learning, potentially impacting future model development and evaluation.

RANK_REASON This is a research paper published on arXiv detailing new theoretical bounds and empirical results for a specific statistical estimation problem.

Read on arXiv stat.ML →

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

New bounds improve discrete probability distribution estimation

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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Doron Cohen, Aryeh Kontorovich, Yonatan Livshitz ·

    Improved Distribution Estimation in $\ell_\infty$

    arXiv:2605.30509v1 Announce Type: new Abstract: We present improved bounds for estimating discrete probability distributions under the $\ell_\infty$ norm. These include minimax bounds in expectation and high-probability tail bounds. We resolve some of the open questions posed in …

  2. arXiv stat.ML TIER_1 English(EN) · Yonatan Livshitz ·

    Improved Distribution Estimation in $\ell_\infty$

    We present improved bounds for estimating discrete probability distributions under the $\ell_\infty$ norm. These include minimax bounds in expectation and high-probability tail bounds. We resolve some of the open questions posed in Kontorovich and Painsky (JMLR, 2025) -- includin…

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