Learning Distributionally Robust First-Order Methods for Convex Optimization

We propose a distributionally robust approach to learning hyperparameters (e.g., step sizes and momentum coefficients) for first-order methods in convex optimization. Given a dataset of problem instances, we minimize a Wasserstein distributionally robust version of the performance estimation problem