English(EN) High-Dimensional Gaussian Mean Estimation under Realizable Contamination

高斯均值估计面临信息-计算鸿沟

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-15 04:00

研究人员在特定污染模型下识别出了高斯均值估计的信息-计算鸿沟。该鸿沟表明，高效算法需要比理论上可能的多得多样本，或者会产生指数级运行时间。该研究通过一个几乎匹配这种权衡的算法，补充了统计查询模型中的下界，从而全面理解了问题的复杂性。 AI

影响确立了特定污染模型下数据处理的理论极限，可能影响鲁棒AI系统的设计。

排序理由该集群包含一篇详细介绍机器学习理论研究成果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

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arXiv stat.ML TIER_1 English(EN) · Ilias Diakonikolas, Daniel M. Kane, Thanasis Pittas · 2026-07-15 04:00

高维高斯均值估计在可实现污染下的研究

arXiv:2603.16798v2 Announce Type: replace-cross Abstract: We study mean estimation for a Gaussian distribution with identity covariance in $\mathbb{R}^d$ under a missing data scheme termed realizable $\epsilon$-contamination model. In this model an adversary can choose a function…