Before revision:  From a practical perspective, our method is very flexible and easy to use: In both steps, we can use any method of our choice , e.g., penalized regression, a deep net , or boosting;
After revision:  From a practical perspective, our method is very flexible and easy to use: In both steps, we can use any loss-minimization method , e.g., penalized regression, a deep net , or boosting;