We propose methods for density estimation and data synthesis using a novel
form of unsupervised random forests. Inspired by generative adversarial
networks, we implement a recursive procedure in which trees gradually learn
structural properties of the data through alternating rounds of generation and
discrimination. The method is provably consistent under minimal assumptions.
Unlike classic tree-based alternatives, our approach provides smooth
(un)conditional densities and allows for fully synthetic data generation. We
achieve comparable or superior performance to state-of-the-art probabilistic
circuits and deep learning models on various tabular data benchmarks while
executing about two orders of magnitude faster on average. An accompanying
$\texttt{R}$ package, $\texttt{arf}$, is available on $\texttt{CRAN}$.

本文提出了使用无监督随机森林的新型密度估计和数据合成方法。通过交替的生成和区分，树逐渐学习到数据的结构属性。 与经典的基于树的替代方案不同，我们的方法提供平滑的（非）条件密度并允许完全合成数据生成。 在执行速度平均快大约两个数量级的同时，我们在各种表格数据基准测试上实现了与最先进的概率电路和深度学习模型可比或更好的性能。