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新界限改进离散概率分布估计

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员发布了使用 $\ell_\infty$ 范数估计离散概率分布的新界限。该工作提供了期望的 minimax 界限和高概率尾部界限。这项研究解决了 Kontorovich 和 Painsky (JMLR, 2025) 中悬而未决的问题，包括他们最紧风险界限的经验版本以及最坏情况极端分布的识别。 AI

影响 推进统计机器学习的理论理解，可能影响未来的模型开发和评估。

排序理由 这是一篇发表在 arXiv 上的研究论文，详细介绍了特定统计估计问题的新的理论界限和经验结果。

在 arXiv stat.ML 阅读 →

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新界限改进离散概率分布估计

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这是一篇发表在 arXiv 上的研究论文，详细介绍了特定统计估计问题的新的理论界限和经验结果。
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报道来源 [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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