  Neural Architecture Search (NAS) methods have shown to output networks that
largely outperform human-designed networks. However, conventional NAS methods
have mostly tackled the single dataset scenario, incuring in a large
computational cost as the procedure has to be run from scratch for every new
dataset. In this work, we focus on predictor-based algorithms and propose a
simple and efficient way of improving their prediction performance when dealing
with data distribution shifts. We exploit the Kronecker-product on the randomly
wired search-space and create a small NAS benchmark composed of networks
trained over four different datasets. To improve the generalization abilities,
we propose GRASP-GCN, a ranking Graph Convolutional Network that takes as
additional input the shape of the layers of the neural networks. GRASP-GCN is
trained with the not-at-convergence accuracies, and improves the
state-of-the-art of 3.3 % for Cifar-10 and increasing moreover the
generalization abilities under data distribution shift.
