  We consider the problem of conditional density estimation, which is a major
topic of interest in the fields of statistical and machine learning. Our
method, called Marginal Contrastive Discrimination, MCD, reformulates the
conditional density function into two factors, the marginal density function of
the target variable and a ratio of density functions which can be estimated
through binary classification. Like noise-contrastive methods, MCD can leverage
state-of-the-art supervised learning techniques to perform conditional density
estimation, including neural networks. Our benchmark reveals that our method
significantly outperforms in practice existing methods on most density models
and regression datasets.
