Black-Box Generalization: Stability of Zeroth-Order Learning

We provide the first generalization error analysis for black-box learning through derivative-free optimization. Under the assumption of a Lipschitz and smooth unknown loss, we consider the Zeroth-order Stochastic Search (ZoSS) algorithm, that updates a d-dimensional model by replacing stochastic gradient directions with stochastic differences of K+1 perturbed loss evaluations per... Show more