Adversarial Random Forests for Density Estimation and Generative Modeling

David Watson, Kristin Blesch, Jan Kapar, Marvin N. Wright

Abstract: 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 𝑅 package, 𝑎𝑟𝑓, is available on 𝐶𝑅𝐴𝑁.