1. Our contribution. The DRLR informed KNN builds on a previously proposed method using OLS to inform

KNN. Y et, our key, differentiating contributions lie in that: It is hard to define "optimal Different from the classical metric learning works (e.g., Gottlieb et al. Gottlieb et al. [2017] focuses on the computational aspect of solving the metric regression problem. They significantly improve the computational efficiency compared to solving a convex program. A direct comparison of the two papers easily shows how different they are. Gottlieb et al. [2017] studies only the regression problem, whereas we considered a richer framework of Theorem 5.1 in their paper provided a risk bound that depends on the empirical risk We would be happy to update our literature review to make these connections.