• Corpus ID: 252846366

Adversarial random forests for density estimation and generative modeling

@inproceedings{Watson2022AdversarialRF,
  title={Adversarial random forests for density estimation and generative modeling},
  author={David Watson and Kristin Blesch and Jan Kapar and Marvin N. Wright},
  year={2022}
}
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… 
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