论文标题
MCD：有条件密度估计的边际对比歧视
MCD: Marginal Contrastive Discrimination for conditional density estimation
论文作者
论文摘要
我们考虑条件密度估计的问题，这是统计和机器学习领域中有趣的主题。我们的方法称为边缘对比歧视，MCD将条件密度函数重新划分为两个因素：目标变量的边际密度函数以及可以通过二进制分类估算的密度函数比率。像噪声对抗性方法一样，MCD可以利用最新的监督学习技术来执行有条件的密度估计，包括神经网络。我们的基准表明，在大多数密度模型和回归数据集上，我们的方法在实践中明显胜过现有方法。
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.
