  A procedure for unfolding the true distribution from experimental data is
presented. Machine learning methods are applied for simultaneous identification
of an apparatus function and solving of an inverse problem. A priori
information about the true distribution from theory or previous experiments is
used for Monte-Carlo simulation of the training sample. The training sample can
be used to calculate a transformation from the true distribution to the
measured one. This transformation provides a robust solution for an unfolding
problem with minimal biases and statistical errors for the set of distributions
used to create the training sample. The dimensionality of the solved problem
can be arbitrary. A numerical example is presented to illustrate and validate
the procedure.
