  Approximating probability distributions can be a challenging task,
particularly when they are supported over regions of high geometrical
complexity or exhibit multiple modes. Annealing can be used to facilitate this
task which is often combined with constant a priori selected increments in
inverse temperature. However, using constant increments limit the computational
efficiency due to the inability to adapt to situations where smooth changes in
the annealed density could be handled equally well with larger increments. We
introduce AdaAnn, an adaptive annealing scheduler that automatically adjusts
the temperature increments based on the expected change in the Kullback-Leibler
divergence between two distributions with a sufficiently close annealing
temperature. AdaAnn is easy to implement and can be integrated into existing
sampling approaches such as normalizing flows for variational inference and
Markov chain Monte Carlo. We demonstrate the computational efficiency of the
AdaAnn scheduler for variational inference with normalizing flows on a number
of examples, including density approximation and parameter estimation for
dynamical systems.
