Robust methods for high-dimensional linear learning
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We propose statistically robust and computationally efficient linear learning methods in the high-dimensional batch setting, where the number of features d may exceed the sample size n. We employ, in a generic learning setting, two algorithms depending on whether the considered loss function is gradient-Lipschitz or not. Then, we instantiate our framework on several applications including vanilla sparse, group-sparse and low-rank matrix recovery. This leads, for each application, to efficient and robust learning algorithms, that reach near-optimal estimation rates under heavy-tailed distributions and the presence of outliers. For vanilla s-sparsity, we are able to reach the slog (d)/n rate under heavy-tails and η-corruption, at a computational cost comparable to that of non-robust analogs. We provide an efficient implementation of our algorithms in an open-source 𝙿𝚢𝚝𝚑𝚘𝚗 library called 𝚕𝚒𝚗𝚕𝚎𝚊𝚛𝚗, by means of which we carry out numerical experiments which confirm our theoretical findings together with a comparison to other recent approaches proposed in the literature.
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