Before revision: We consider making outputs of the greedy algorithm for monotone submodular function maximization differentiable w.r.t. parameters of objective functions; this is motivated bymany applications , e.g., sensitivity analysis and end-to-end learning .
After revision: Motivated by , e.g., sensitivity analysis and end-to-end learning .