  We present a novel strategy aimed at restoring correct convergence in complex
Langevin simulations. The central idea is to incorporate system-specific prior
knowledge into the simulations, in order to circumvent the NP-hard sign
problem. In order to do so, we modify complex Langevin using kernels and
propose the use of modern auto-differentiation methods to learn optimal kernel
values. The optimization process is guided by functionals encoding relevant
prior information, such as symmetries or Euclidean correlator data. Our
approach recovers correct convergence in the non-interacting theory on the
Schwinger-Keldysh contour for any real-time extent. For the strongly coupled
quantum anharmonic oscillator we achieve correct convergence up to three-times
the real-time extent of the previous benchmark study. An appendix sheds light
on the fact that for correct convergence not only the absence of boundary
terms, but in addition the correct Fokker-Plank spectrum is crucial.
