text: The paper proposes to improve the kernel approximation of random features by using quadratures, in particular, stochastic spherical-radial rules. The quadrature rules have smaller variance given the same number of random features, and experiments show its reconstruction error and classification accuracies are better than existing algorithms. It is an interesting paper, but it seems the authors are not aware of some existing works [1, 2] on quadrature for random features. Given these previous works, the contribution and novelty of the paper is limited. [1] Francis Bach. On the Equivalence between Kernel Quadrature Rules and Random Feature Expansions. JMLR, 2017. [2] Tri Dao, Christopher De Sa, Christopher Ré. Gaussian Quadrature for Kernel Features. NIPS 2017