论文标题
对抗性随机森林，用于密度估计和生成建模
Adversarial random forests for density estimation and generative modeling
论文作者
论文摘要
我们提出了使用一种新型的无监督随机森林形式的密度估计和数据合成的方法。受生成对抗网络的启发，我们实施了一个递归程序，在该过程中，树木通过发电和歧视的交替回合逐渐学习数据的结构属性。在最小的假设下，该方法是一致的。与经典的基于树的替代方案不同，我们的方法提供了光滑的（UN）条件密度，并允许完全合成的数据生成。我们在各种表格数据基准上实现了与最先进的概率电路和深度学习模型的可比性或卓越的性能，同时平均执行大约两个数量级。随附的$ \ texttt {r} $软件包，$ \ texttt {arf} $，可在$ \ texttt {cran} $上获得。
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 $\texttt{CRAN}$.
