Robust Sparse Estimation for Gaussians with Optimal Error under Huber
  Contamination
Ilias Diakonikolas Daniel M. Kane Sushrut Karmalkar Ankit Pensia Thanasis Pittas
We study Gaussian sparse estimation tasks in Huber's contamination model with
a focus on mean estimation, PCA, and linear regression. For each of these
tasks, we give the first sample and computationally efficient robust estimators
with optimal error guarantees, within constant factors. All prior efficient
algorithms for these tasks incur quantitatively suboptimal error. Concretely,
for Gaussian robust $k$-sparse mean estimation on $\mathbb{R}^d$ with
corruption rate $\epsilon>0$, our algorithm has sample complexity
$(k^2/\epsilon^2)\mathrm{polylog}(d/\epsilon)$, runs in sample polynomial time,
and approximates the target mean within $\ell_2$-error $O(\epsilon)$. Previous
efficient algorithms inherently incur error $\Omega(\epsilon
\sqrt{\log(1/\epsilon)})$. At the technical level, we develop a novel
multidimensional filtering method in the sparse regime that may find other
applications.