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- W4301366938 abstract "We introduce a criterion, resilience, which allows properties of a dataset (such as its mean or best low rank approximation) to be robustly computed, even in the presence of a large fraction of arbitrary additional data. Resilience is a weaker condition than most other properties considered so far in the literature, and yet enables robust estimation in a broader variety of settings. We provide new information-theoretic results on robust distribution learning, robust estimation of stochastic block models, and robust mean estimation under bounded $k$th moments. We also provide new algorithmic results on robust distribution learning, as well as robust mean estimation in $ell_p$-norms. Among our proof techniques is a method for pruning a high-dimensional distribution with bounded $1$st moments to a stable core with bounded $2$nd moments, which may be of independent interest." @default.
- W4301366938 created "2022-10-05" @default.
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- W4301366938 date "2017-03-15" @default.
- W4301366938 modified "2023-09-27" @default.
- W4301366938 title "Resilience: A Criterion for Learning in the Presence of Arbitrary Outliers" @default.
- W4301366938 doi "https://doi.org/10.48550/arxiv.1703.04940" @default.
- W4301366938 hasPublicationYear "2017" @default.
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