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：我们研究了条件密度估计问题，这是统计与机器学习领域的重要课题。本文提出的方法称为边际对比判别法（MCD），该方法将条件密度函数重构为两个因子：目标变量的边缘密度函数和一个可通过二元分类估计的密度函数比值。与噪声对比方法类似，MCD能够利用包括神经网络在内的先进监督学习技术进行条件密度估计。实验结果表明，在大多数密度模型与回归数据集上，本方法显著优于现有方法。