AI RESEARCH
High-Dimensional Gaussian Mean Estimation under Realizable Contamination
arXiv CS.LG
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ArXi:2603.16798v1 Announce Type: new 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 $r(x)$ between 0 and $\epsilon$ and each sample $x$ goes missing with probability $r(x