  We investigate a problem estimating coefficients of linear regression under
sparsity assumption when covariates and noises are sampled from heavy tailed
distributions. Additionally, we consider the situation where not only
covariates and noises are sampled from heavy tailed distributions but also
contaminated by outliers. Our estimators can be computed efficiently, and
exhibit sharp error bounds.
