Agnostic learning and multiaccuracy are computationally equivalent problems—you can efficiently convert solutions between them using adaptive queries, meaning advances in one area directly enable progress in the other.
This paper proves that two fundamental machine learning tasks are computationally equivalent: training a discriminative model to classify data as well as possible, and training a generative model whose outputs fool all classifiers in a given model class.