Robust Estimators in High-Dimensions Without the Computational Intractability
SIAM Journal on Computing, pp. 742-864, 2019.
We study high-dimensional distribution learning in an agnostic setting where an adversary is allowed to arbitrarily corrupt an $varepsilon$-fraction of the samples. Such questions have a rich history spanning statistics, machine learning, and theoretical computer science. Even in the most basic settings, the only known approaches are eith...More
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