Interpolators -- estimators that achieve zero training error -- have attracted growing attention in machine learning, mainly because state-of-the art neural networks appear to be models of this type. In this paper, we study minimum $ell_2$ norm (``ridgeless) interpolation in high-dimensional least squares regression. We consider two different models for the feature distribution: a linear model, where the feature vectors $x_i in {mathbb R}^p$ are obtained by applying a linear transform to a vector of i.i.d. entries, $x_i = Sigma^{1/2} z_i$ (with $z_i in {mathbb R}^p$); and a nonlinear model, where the feature vectors are obtained by passing the input through a random one-layer neural network, $x_i = varphi(W z_i)$ (with $z_i in {mathbb R}^d$, $W in {mathbb R}^{p times d}$ a matrix of i.i.d. entries, and $varphi$ an activation function acting componentwise on $W z_i$). We recover -- in a precise quantitative way -- several phenomena that have been observed in large-scale neural networks and kernel machines, including the double descent behavior of the prediction risk, and the potential benefits of overparametrization.
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