Before revision:  The results are applied to three open problems lying at the interface of applied probability and computational learning theory. We find that the proposed models can approximate any regular conditional distribution of a \mathcal{Y-valued random element Y depending on an \mathcal{X}-valued random element X, with arbitrarily high probability.
After revision:  -valued random element Y depending on an \mathcal{X}-valued random element X, with arbitrarily high probability.