Abstract: We study the statistical performance of minimum norm interpolators in non-linear regression under additive Gaussian noise. Specifically, we focus on norms that satisfy either $2$-uniformly convexity or the cotype $2$ property -- these include inner-product spaces, $\ell_{p}$ norms, and $W_{p}$ Sobolev spaces for $1 \leq p \leq 2$. Our main result demonstrates that under $2$-uniform convexity, the bias of the minimal norm solution is bounded by the Gaussian complexity of the class. We then prove an Efron-Stein type estimate for the variance of the minimal norm solution under cotype $2$ or $2$-uniform convexity. Our approach leverages tools from the local theory of finite dimensional Banach spaces, and to the best of our knowledge, it is the first to study non-linear models that are ``far'' from Hilbert spaces.