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A spectral algorithm for robust regression with subgaussian rates

We study a new linear up to quadratic time algorithm for linear regression in the absence of strong assumptions on the underlying distributions of samples, and in the presence of outliers. The goal is to design a procedure which comes with actual working code that attains the optimal sub-gaussian error bound even though the data have only finite moments (up to $L_4$) and in the presence of possibly adversarial outliers. A polynomial-time solution to this problem has been recently discovered but has high runtime due to its use of Sum-of-Square hierarchy programming. At the core of our algorithm is an adaptation of the spectral method introduced for the mean estimation problem to the linear regression problem. As a by-product we established a connection between the linear regression problem and the furthest hyperplane problem. From a stochastic point of view, in addition to the study of the classical quadratic and multiplier processes we introduce a third empirical process that comes naturally in the study of the statistical properties of the algorithm.

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