Learning Sampling Strategies for Stochastic Gradient Descent using Deep Reinforcement Learning techniques
Solving finite-sum minimization problems could be done by the use of a gradient descent algorithm. The algorithm evaluates the gradient with respect to the current state of the parameters and updates the parameters in the direction of the steepest descent. For certain problems, evaluating the full gradient at every iteration is computationally heavy and the gradient of the objective function is th
