We introduce the first “learned” scheme for implementing a compressed rank/select dictionary. We prove theoretical bounds on its time and space performance both in the worst case and in the case of input distributions with finite mean and variance. We corroborate these theoretical results with a large set of experiments over datasets originating from a variety of sources and applications (Web, DNA sequencing, information retrieval and natural language processing), and we show that a carefully engineered version of our approach provides new interesting space-time trade-offs with respect to several well-established solutions.