Reviews: Task-based End-to-end Model Learning in Stochastic Optimization

The main idea of the paper is to learn a predictive model p(y x;theta) such that the task's objective function f is directly optimized. In contrast, traditional approaches learn p(y x;theta) to minimize a prediction error without considering f. The main technical challenge in the paper is to solve a sub-optimization problem involving argmin w.r.t.