Algorithms for heavy-tailed statistics: regression, covariance estimation, and beyond

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 exponentially-good success probability in the face of heavy-tailed data.