Adversarial random forests for density estimation and generative modelling. (arXiv:2205.09435v2 [stat.ML] UPDATED)

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 existing tree-based alternatives, our approach provides smooth
unconditional densities and allows for fully synthetic data generation. We
achieve comparable or superior performance to state-of-the-art deep learning
models …

arxiv modelling random random forests