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.

翻译：我们考虑的是有条件密度估计问题,这是统计和机器学习领域关注的一个主要主题。我们的方法,称为边际对比歧视,MCD,将有条件密度函数重新分为两个因素:目标变量的边际密度函数和密度函数比率,可以通过二进制分类来估计。像噪音反射方法一样,MCD可以利用最先进的受监督的学习技术来进行有条件密度估计,包括神经网络。我们的基准显示,我们的方法在实践上大大优于大多数密度模型和回归数据集的现有方法。