Algorithms for Heavy-Tailed Statistics: Regression, Covariance Estimation, and Beyond
STOC '20: 52nd Annual ACM SIGACT Symposium on Theory of Computing Chicago IL USA June, 2020, pp. 601-609, 2019.
We study polynomial-time algorithms for linear regression and covariance estimation in the absence of strong (Gaussian) assumptions on the underlying distributions of samples, making assumptions instead about only finitely-many moments. We focus on how many samples are required to perform estimation and regression with high accuracy and e...More
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