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Smooth densities and generative modeling with unsupervised random forests. (arXiv:2205.09435v1 [stat.ML])
May 20, 2022, 1:12 a.m. | David S. Watson, Kristin Blesch, Jan Kapar, Marvin N. Wright
cs.LG updates on arXiv.org arxiv.org
Density estimation is a fundamental problem in statistics, and any attempt to
do so in high dimensions typically requires strong assumptions or complex deep
learning architectures. An important application for density estimators is
synthetic data generation, an area currently dominated by neural networks that
often demand enormous training datasets and extensive tuning. We propose a new
method based on unsupervised random forests for estimating smooth densities in
arbitrary dimensions without parametric constraints, as well as generating
realistic synthetic data. We …
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