Before revision: Differential machine learning (ML) extends supervised learning, with models trained on examples of not only inputs and labels , but also differentials of labels to inputs.
After revision: Differential machine learning combines automatic adjoint differentiation (AAD) with modern machine learning (ML) in the context of risk management of financial Derivatives. We introduce novel algorithms for training fast, accurate pricing and risk approximations, online, in real-time, with convergence guarantees. Our machinery is applicable to arbitrary Derivatives instruments or trading books, under arbitrary stochastic models of the underlying market variables. It effectively resolves computational bottlenecks of Derivatives risk reports and capital calculations. Differential ML is a general extension of supervised learning, with models trained on examples of not only inputs and labels , but also differentials of labels to inputs.