Watson, David S; Blesch, Kristin; Kapar, Jan; Wright, Marvin N; (2023) Adversarial Random Forests for Density Estimation and Generative Modeling. In: Ruiz, Francisco and Dy, Jennifer and Van de Meent, Jan-Willem, (eds.) Proceedings of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS) 2023. PMLR (The Proceedings of Machine Learning Research): Palau de Congressos, Valencia, Spain.

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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-ofthe-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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