Paper improves privacy in machine learning optimization.
problem Efficiently private empirical risk minimization for smooth objectives.
method Gradient descent with output perturbation for strongly convex objectives; RRPSGD for non-convex objectives.
result Proves improved utility and running time for private optimization.