Efficient Algorithms and Lower Bounds for Robust Linear Regression

We study the problem of high-dimensional linear regression in a robust model where an $ε$-fraction of the samples can be adversarially corrupted. We focus on the fundamental setting where the covariates of the uncorrupted samples are drawn from a Gaussian distribution $\mathcal{N}(0, Σ)$ on $\mathbb{R}^d$. We give nearly tight upper bounds and computational lower bounds for this problem. Specifically, our main contributions are as follows: For the case that the covariance matrix is known to be the identity, we give a sample near-optimal and computationally efficient algorithm that outputs a candidate hypothesis vector $\widehatβ$ which approximates the unknown regression vector $β$ within $\ell_2$-norm $O(ε\log(1/ε) σ)$, where $σ$ is the standard deviation of the random observation noise. An error of $Ω(εσ)$ is information-theoretically necessary, even with infinite sample size. Prior work gave an algorithm for this problem with sample complexity $\tildeΩ(d^2/ε^2)$ whose error guarantee scales with the $\ell_2$-norm of $β$. For the case of unknown covariance, we show that we can efficiently achieve the same error guarantee as in the known covariance case using an additional $\tilde{O}(d^2/ε^2)$ unlabeled examples. On the other hand, an error of $O(εσ)$ can be information-theoretically attained with $O(d/ε^2)$ samples. We prove a Statistical Query (SQ) lower bound providing evidence that this quadratic tradeoff in the sample size is inherent. More specifically, we show that any polynomial time SQ learning algorithm for robust linear regression (in Huber's contamination model) with estimation complexity $O(d^{2-c})$, where $c>0$ is an arbitrarily small constant, must incur an error of $Ω(\sqrtε σ)$.