Paper
Robustness meets algorithms
Published Apr 26, 2021 · Ilias Diakonikolas, Gautam Kamath, D. Kane
Communications of the ACM
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Abstract
In every corner of machine learning and statistics, there is a need for estimators that work not just in an idealized model, but even when their assumptions are violated. Unfortunately, in high dimensions, being provably robust and being efficiently computable are often at odds with each other. We give the first efficient algorithm for estimating the parameters of a high-dimensional Gaussian that is able to tolerate a constant fraction of corruptions that is independent of the dimension. Prior to our work, all known estimators either needed time exponential in the dimension to compute or could tolerate only an inverse-polynomial fraction of corruptions. Not only does our algorithm bridge the gap between robustness and algorithms, but also it turns out to be highly practical in a variety of settings.
Our efficient algorithm for estimating high-dimensional Gaussian parameters allows for a constant fraction of corruptions, bridging the gap between robustness and algorithms, and is highly practical in various settings.
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