Review for NeurIPS paper: Theoretical Insights Into Multiclass Classification: A High-dimensional Asymptotic View

The authors characterize the asymptotic behaviour of four linear classifiers applied to data generated according to two models. The four classifiers differ according to their loss function: least-squares, class averaging, weighted least-squares and cross-entropy. The data are obtained through a Gaussian mixture or a multinomial logit model. The main results are convergences in probability of the parameters (intercepts and "correlation" matrices). The total and class-wise accuracies are also characterized. Experimental results (obtained on artificial data following the aforementioned models) are also provided in Section 5.