用辅助样本将条件密度估计转为单一回归任务，提升高维数据表现。
Transforming Conditional Density Estimation Into a Single Nonparametric Regression Task
- 引入辅助样本，将条件密度估计转化为单个非参数回归问题。
- 在合成数据和真实数据集上均达到或超越现有最优方法。
- 适合需要快速部署且无需复杂调参的实证研究者使用。
我们提出一种通过引入辅助样本，将条件密度估计问题转化为单一非参数回归任务的方法。该方法可有效利用在高维场景下表现优异的回归模型，如神经网络和决策树。主要理论结果证明了该估计器在数据量趋于无穷时收敛至真实条件密度。我们开发了名为condensité的方法来实现该思路。在合成数据上验证了辅助样本的有效性，并展示了condensité可实现开箱即用的良好效果。我们在大规模人口调查数据集和卫星遥感数据集上进行了评估，结果表明condensité在两种场景下均达到或超过当前最优性能，生成的条件密度分布与已有文献中的发现一致。本工作为基于回归的条件密度估计开辟了新路径，实证结果展现出在应用研究中的巨大潜力。
原文摘要 · Abstract (English)
We propose a way of transforming the problem of conditional density estimation into a single nonparametric regression task via the introduction of auxiliary samples. This allows leveraging regression methods that work well in high dimensions, such as neural networks and decision trees. Our main theoretical result characterizes and establishes the convergence of our estimator to the true conditional density in the data limit. We develop condensité, a method that implements this approach. We demonstrate the benefit of the auxiliary samples on synthetic data and showcase that condensité can achieve good out-of-the-box results. We evaluate our method on a large population survey dataset and on a satellite imaging dataset. In both cases, we find that condensité matches or outperforms the state of the art and yields conditional densities in line with established findings in the literature on each dataset. Our contribution opens up new possibilities for regression-based conditional density estimation and the empirical results indicate strong promise for applied research.
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