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The paper deals with the task of robust nonlinear regression in the presence of outliers. The problem is dealt in the context of reproducing kernel Hilbert spaces (RKHS). In contrast to more classical approaches, a recent trend is to model the outliers as a sparse vector noise component and mobilize tools from the sparsity-aware/compressed sensing theory to impose sparsity on it. In this paper, three...