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Mar, 2024
稳健稀疏均值估计的次二次时间算法
A Sub-Quadratic Time Algorithm for Robust Sparse Mean Estimation
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TL;DR
稀疏均值估计算法在存在对抗性异常值的情况下的研究,提出了一种在次二次时间内运行的鲁棒稀疏均值估计算法,并在检测弱相关性方面取得了算法进展。
Abstract
We study the algorithmic problem of
sparse mean estimation
in the presence of
adversarial outliers
. Specifically, the algorithm observes a \emph{corrupted} set of samples from $\mathcal{N}(\mu,\mathbf{I}_d)$, whe
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