  Machine learning is used to compute achievable information rates (AIRs) for a
simplified fiber channel. The approach jointly optimizes the input distribution
(constellation shaping) and the auxiliary channel distribution to compute AIRs
without explicit channel knowledge in an end-to-end fashion.
