
arfpy: A Python Package for Density Estimation and Generative Modeling with Adversarial Random Forests
By: Kristin Blesch and Marvin N. Wright
References
- Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y. Generative adversarial nets. Advances in Neural Information Processing Systems. 2014; 27.
- Xu L, Skoularidou M, Cuesta-Infante A, Veeramachaneni K. Modeling tabular data using conditional GAN. Advances in Neural Information Processing Systems. 2019; 32.
- Kingma DP, Welling M. Auto-encoding variational bayes. International Conference on Learning Representations;
2014 . - Rezende D, Mohamed S. Variational inference with normalizing flows. Proceedings of the 32nd International Conference on Machine Learning.
2015 ; 37: 1530–1538. - Ho J, Jain A, Abbeel P. Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems. 2020; 33: 6840–6851.
- Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I. Attention is all you need. Advances in Neural Information Processing Systems. 2017; 30.
- Watson DS, Blesch K, Kapar J, Wright MN. Adversarial random forests for density estimation and generative modeling. International Conference on Artificial Intelligence and Statistics. PMLR.
2023 ; 206: 5357–5375. - Breiman L. Random forests. Machine learning. 2001; 45: 5–32. DOI: 10.1023/A:1010933404324
- Grinsztajn L, Oyallon E, Varoquaux G. Why do tree-based models still outperform deep learning on typical tabular data? Advances in Neural Information Processing Systems. 2022; 35: 507–520.
- Nock R, Guillame-Bert M. Generative forests. arXiv Preprint 2308.03648; 2023. DOI: 10.48550/arXiv.2308.03648
- Wright MN, Watson DS. arf: Adversarial Random Forests; 2023. URL
https://CRAN.R-project.org/package=arf . R package version 0.1.3. - Gautier L, et al. rpy2: Python-r bridge. GitHub repository; 2023. URL
https://github.com/rpy2/rpy2 . - van der Schaar Lab. synthcity: A library for generating and evaluating synthetic tabular data. GitHub repository; 2023. URL
https://github.com/vanderschaarlab/synthcity . - Shi T, Horvath S. Unsupervised learning with random forest predictors. Journal of Computational and Graphical Statistics. 2006; 15(1): 118–138. DOI: 10.1198/106186006X94072
DOI: https://doi.org/10.5334/jors.492 | Journal eISSN: 2049-9647
Language: English
Submitted on: Nov 20, 2023
Accepted on: Apr 11, 2024
Published on: May 1, 2024
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
© 2024 Kristin Blesch, Marvin N. Wright, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.