  We study the classical problem of recovering a multidimensional source
process from observations of nonlinear mixtures of this process. Assuming
statistical independence of the coordinate processes of the source, we show
that this recovery is possible for many popular models of stochastic processes
(up to order and monotone scaling of their coordinates) if the mixture is given
by a sufficiently differentiable, invertible function. Key to our approach is
the combination of tools from stochastic analysis and recent contrastive
learning approaches to nonlinear ICA. This yields a scalable method with widely
applicable theoretical guarantees for which our experiments indicate good
performance.
