Title: Differentially Private Learning with Margin Guarantees Abstract: Preserving privacy is a crucial objective for machine learning algorithms. But, despite the remarkable theoretical and algorithmic progress in differential privacy over the last decade or more, its application to learning still faces several obstacles. A recent series of publications have shown that differentially private PAC learning of infinite hypothesis sets is not possible, even for common hypothesis sets…