# Adversarial random forests for density estimation and generative modeling

arXiv: https://arxiv.org/abs/2205.09435 · HTML: https://mlsanity.com/paper/2205.09435/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- Builds on 42 of its 137 references that are in the map.
- First posted on arXiv 19 May 2022.

## Builds on (most cited first, 30 of 42)

- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Normalizing Flows for Probabilistic Modeling and Inference](https://mlsanity.com/paper/1912.02762/) (2019)
- [Variational Diffusion Models](https://mlsanity.com/paper/2107.00630/) (2021)
- [Pixel Recurrent Neural Networks](https://mlsanity.com/paper/1601.06759/) (2016)
- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [Estimation and Inference of Heterogeneous Treatment Effects using Random Forests](https://mlsanity.com/paper/1510.04342/) (2015)
- [Efficient Content-Based Sparse Attention with Routing Transformers](https://mlsanity.com/paper/2003.05997/) (2020)
- [Generalized Random Forests](https://mlsanity.com/paper/1610.01271/) (2016)
- [Generating Multi-label Discrete Patient Records using Generative Adversarial Networks](https://mlsanity.com/paper/1703.06490/) (2017)
- [Learning Bayesian Networks: The Combination of Knowledge and Statistical Data](https://mlsanity.com/paper/1302.6815/) (2013)
- [A Random Forest Guided Tour](https://mlsanity.com/paper/1511.05741/) (2015)
- [Classification Accuracy Score for Conditional Generative Models](https://mlsanity.com/paper/1905.10887/) (2019)
- [Constructing Unrestricted Adversarial Examples with Generative Models](https://mlsanity.com/paper/1805.07894/) (2018)
- [Synthetic Data -- Anonymisation Groundhog Day](https://mlsanity.com/paper/2011.07018/) (2020)
- [Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits](https://mlsanity.com/paper/2004.06231/) (2020)
- [Consistency of random forests](https://mlsanity.com/paper/1405.2881/) (2014)
- [Generative Models for Effective ML on Private, Decentralized Datasets](https://mlsanity.com/paper/1911.06679/) (2019)
- [Hyperparameters and Tuning Strategies for Random Forest](https://mlsanity.com/paper/1804.03515/) (2018)
- [How good is my GAN?](https://mlsanity.com/paper/1807.09499/) (2018)
- [CTAB-GAN+: Enhancing Tabular Data Synthesis](https://mlsanity.com/paper/2204.00401/) (2022)
- [Structure Learning in Graphical Modeling](https://mlsanity.com/paper/1606.02359/) (2016)
- [Classification accuracy as a proxy for two sample testing](https://mlsanity.com/paper/1602.02210/) (2016)
- [Tractable Regularization of Probabilistic Circuits](https://mlsanity.com/paper/2106.02264/) (2021)
- [Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression](https://mlsanity.com/paper/2005.14458/) (2020)
- [LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation](https://mlsanity.com/paper/1703.01560/) (2017)

## Built on by (most cited first, 30 of 44)

- [Continuous Diffusion for Mixed-Type Tabular Data](https://mlsanity.com/paper/2312.10431/) (2023)
- [TabEBM: A Tabular Data Augmentation Method with Distinct Class-Specific Energy-Based Models](https://mlsanity.com/paper/2409.16118/) (2024)
- [A Survey on Deep Learning Approaches for Tabular Data Generation: Utility, Alignment, Fidelity, Privacy, Diversity, and Beyond](https://mlsanity.com/paper/2503.05954/) (2025)
- [Data Plagiarism Index: Characterizing the Privacy Risk of Data-Copying in Tabular Generative Models](https://mlsanity.com/paper/2406.13012/) (2024)
- [LaTable: Towards Large Tabular Models](https://mlsanity.com/paper/2406.17673/) (2024)
- [Conditional Feature Importance for Mixed Data](https://mlsanity.com/paper/2210.03047/) (2022)
- [CountARFactuals -- Generating plausible model-agnostic counterfactual explanations with adversarial random forests](https://mlsanity.com/paper/2404.03506/) (2024)
- [TabMDA: Tabular Manifold Data Augmentation for Any Classifier using Transformers with In-context Subsetting](https://mlsanity.com/paper/2406.01805/) (2024)
- [Privacy Auditing Synthetic Data Release through Local Likelihood Attacks](https://mlsanity.com/paper/2508.21146/) (2025)
- [Targeted synthetic data generation for tabular data via hardness characterization](https://mlsanity.com/paper/2410.00759/) (2024)
- [Ensembling Membership Inference Attacks Against Tabular Generative Models](https://mlsanity.com/paper/2509.05350/) (2025)
- [Synth-MIA: A Testbed for Auditing Privacy Leakage in Tabular Data Synthesis](https://mlsanity.com/paper/2509.18014/) (2025)
- [Cascaded Flow Matching for Heterogeneous Tabular Data with Mixed-Type Features](https://mlsanity.com/paper/2601.22816/) (2026)
- [CasTGAN: Cascaded Generative Adversarial Network for Realistic Tabular Data Synthesis](https://mlsanity.com/paper/2307.00384/) (2023)
- [Autoencoding Random Forests](https://mlsanity.com/paper/2505.21441/) (2025)
- [When Tables Leak: Attacking String Memorization in LLM-Based Tabular Data Generation](https://mlsanity.com/paper/2512.08875/) (2025)
- [TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer](https://mlsanity.com/paper/2501.01216/) (2025)
- [TABFAIRGDT: A Fast Fair Tabular Data Generator using Autoregressive Decision Trees](https://mlsanity.com/paper/2509.19927/) (2025)
- [TabQueryBench: A Query-Centric Benchmark for Synthetic Tabular Data](https://mlsanity.com/paper/2607.03926/) (2026)
- [arfpy: A python package for density estimation and generative modeling with adversarial random forests](https://mlsanity.com/paper/2311.07366/) (2023)
- [TAEGAN: Generating Synthetic Tabular Data For Data Augmentation](https://mlsanity.com/paper/2410.01933/) (2024)
- [NRGBoost: Energy-Based Generative Boosted Trees](https://mlsanity.com/paper/2410.03535/) (2024)
- [How Well Does Your Tabular Generator Learn the Structure of Tabular Data?](https://mlsanity.com/paper/2503.09453/) (2025)
- [What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models](https://mlsanity.com/paper/2504.20687/) (2025)
- [xplainfi: Feature Importance and Statistical Inference for Machine Learning in R](https://mlsanity.com/paper/2603.15306/) (2026)
- [A Sobering Look at Tabular Data Generation via Probabilistic Circuits](https://mlsanity.com/paper/2603.23016/) (2026)
- [Multimodal synthesis of MRI and tabular data with diffusion in a joint latent space via cross-attention](https://mlsanity.com/paper/2605.06699/) (2026)
- [Active Tabular Augmentation via Policy-Guided Diffusion Inpainting](https://mlsanity.com/paper/2605.10315/) (2026)
- [AugMask: Training Diffusion Models on Incomplete Tabular Data via Stochastic Augmentation and Masking](https://mlsanity.com/paper/2606.03347/) (2026)
- [Disjoint Generation of Synthetic Data](https://mlsanity.com/paper/2507.19700/) (2025)

## Abstract (arXiv)

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 textttR package, textttarf, is available on textttCRAN.