Papers › Adversarial random forests for density estimation and generative modeling
Adversarial random forests for density estimation and generative modeling
David S. Watson, Kristin Blesch, Jan Kapar, Marvin N. Wright
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.
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