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• W3067609108 abstract "We study the problem of robustly estimating the mean of a $d$-dimensional distribution given $N$ examples, where most coordinates of every example may be missing and $varepsilon N$ examples may be arbitrarily corrupted. Assuming each coordinate appears in a constant factor more than $varepsilon N$ examples, we show algorithms that estimate the mean of the distribution with information-theoretically optimal dimension-independent error guarantees in nearly-linear time $widetilde O(Nd)$. Our results extend recent work on computationally-efficient robust estimation to a more widely applicable incomplete-data setting." @default.
• W3067609108 created "2020-08-24" @default.
• W3067609108 creator A5076540826 @default.
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• W3067609108 date "2020-08-18" @default.
• W3067609108 modified "2023-10-18" @default.
• W3067609108 title "Robust Mean Estimation on Highly Incomplete Data with Arbitrary Outliers" @default.
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• W3067609108 doi "https://doi.org/10.48550/arxiv.2008.08071" @default.
• W3067609108 hasPublicationYear "2020" @default.
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