We study Gaussian sparse estimation tasks in Huber's contamination model with
a focus on mean estimation, PCA, and linear regression. For each of these
tasks, we give the first sample and computationally efficient robust estimators
with optimal error guarantees, within constant factors. All prior efficient
algorithms for these tasks incur quantitatively suboptimal error. Concretely,
for Gaussian robust $k$-sparse mean estimation on $\mathbb{R}^d$ with
corruption rate $\epsilon>0$, our algorithm has sample complexity
$(k^2/\epsilon^2)\mathrm{polylog}(d/\epsilon)$, runs in sample polynomial time,
and approximates the target mean within $\ell_2$-error $O(\epsilon)$. Previous
efficient algorithms inherently incur error $\Omega(\epsilon
\sqrt{\log(1/\epsilon)})$. At the technical level, we develop a novel
multidimensional filtering method in the sparse regime that may find other
applications.

高斯稀疏估计在 Huber 污染模型中研究，针对均值估计、主成分分析和线性回归三个任务，提出了第一个样本和计算高效的鲁棒估计器，保证了较小的误差，并且在常数因子内达到最优。之前针对这些任务的高效算法都产生了数量上次优的误差。具体而言，对于高斯的鲁棒 k 稀疏均值估计在具有污染率为 ε>0 的 R^d 上，我们的算法具有样本复杂度为 (k^2/ε^2)・polylog (d/ε)，在多项式时间内运行，并且在 L2 误差为 O (ε) 的范围内逼近目标均值。之前的高效算法固有地产生了误差 Ω(ε√log (1/ε))。在技术层面上，我们开发了一种在稀疏情况下的新型多维过滤方法，可能具有其他应用。

Huber 污染下高斯稀疏估计的鲁棒性优化错误

Robust Sparse Estimation for Gaussians with Optimal Error under Huber  Contamination

Nearest-neighbor (NN) procedures are well studied and widely used in both
supervised and unsupervised learning problems. In this paper we are concerned
with investigating the performance of NN-based methods for anomaly detection.
We first show through extensive simulations that NN methods compare favorably
to some of the other state-of-the-art algorithms for anomaly detection based on
a set of benchmark synthetic datasets. We further consider the performance of
NN methods on real datasets, and relate it to the dimensionality of the
problem. Next, we analyze the theoretical properties of NN-methods for anomaly
detection by studying a more general quantity called distance-to-measure (DTM),
originally developed in the literature on robust geometric and topological
inference. We provide finite-sample uniform guarantees for the empirical DTM
and use them to derive misclassification rates for anomalous observations under
various settings. In our analysis we rely on Huber's contamination model and
formulate mild geometric regularity assumptions on the underlying distribution
of the data.

探究最近邻方法在异常检测中的表现，通过综合模拟实验和理论分析得到最近邻方法相对于其他现有算法的优异性，并针对不同维度数据和异常观测提供有限样本一致性保证和分类误差率公式。