mlsanityData to 7 Oct 2026

Adversarial random forests for density estimation and generative modeling

arXiv 2205.09435 ↗First posted 19 May 2022stat.ML cs.AI cs.LGcited by 44

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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.

Key facts. Cited by 44 papers in the map (data through 7 Oct 2026). Builds on 42 of its 137 references that are in the map. First posted on arXiv 19 May 2022.

Builds on

42 of 137 references.

References · 42

  1. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  2. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
  3. 2022Generative Adversarial Networkscited by 5,603
  4. 2021Zero-Shot Text-to-Image Generationcited by 4,276
  5. 2013Auto-Encoding Variational Bayescited by 3,980
  6. 2014Conditional Generative Adversarial Netscited by 2,152
  7. 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,315
  8. 2021Variational Diffusion Modelscited by 1,145
  9. 2016Pixel Recurrent Neural Networkscited by 814
  10. 2019Modeling Tabular data using Conditional GANcited by 695
  11. 2015Estimation and Inference of Heterogeneous Treatment Effects using Random Forestscited by 597
  12. 2020Efficient Content-Based Sparse Attention with Routing Transformerscited by 469
30 more
  1. 2016Generalized Random Forestscited by 309
  2. 2017Generating Multi-label Discrete Patient Records using Generative Adversarial Networkscited by 197
  3. 2013Learning Bayesian Networks: The Combination of Knowledge and Statistical Datacited by 185
  4. 2015A Random Forest Guided Tourcited by 168
  5. 2018Constructing Unrestricted Adversarial Examples with Generative Modelscited by 135
  6. 2019Classification Accuracy Score for Conditional Generative Modelscited by 134
  7. 2020Synthetic Data -- Anonymisation Groundhog Daycited by 105
  8. 2020Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuitscited by 102
  9. 2019Generative Models for Effective ML on Private, Decentralized Datasetscited by 85
  10. 2014Consistency of random forestscited by 84
  11. 2018Hyperparameters and Tuning Strategies for Random Forestcited by 83
  12. 2018How good is my GAN?cited by 74
  13. 2022CTAB-GAN+: Enhancing Tabular Data Synthesiscited by 70
  14. 2016Structure Learning in Graphical Modelingcited by 66
  15. 2016Classification accuracy as a proxy for two sample testingcited by 39
  16. 2021Tractable Regularization of Probabilistic Circuitscited by 39
  17. 2017LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generationcited by 36
  18. 2020Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regressioncited by 36
  19. 2017To tune or not to tune the number of trees in random forest?cited by 33
  20. 2019Randomization as Regularization: A Degrees of Freedom Explanation for Random Forest Successcited by 30
  21. 2021Probabilistic Generating Circuitscited by 24
  22. 2022Invertible Tabular GANs: Killing Two Birds with OneStone for Tabular Data Synthesiscited by 24
  23. 2017Random Forest Missing Data Algorithmscited by 23
  24. 2020Joints in Random Forestscited by 21
  25. 2019Asymptotic Distributions and Rates of Convergence for Random Forests via Generalized U-statisticscited by 18
  26. 2013Narrowing the Gap: Random Forests In Theory and In Practicecited by 17
  27. 2021LinCDE: Conditional Density Estimation via Lindsey's Methodcited by 13
  28. 2020Metrics and methods for robustness evaluation of neural networks with generative modelscited by 12
  29. 2017AutoEncoder by Forestcited by 8
  30. 2019Contrast Trees and Distribution Boostingcited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 3; 2024: 8; 2025: 14; 2026: 18.

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  1. 2023Continuous Diffusion for Mixed-Type Tabular Datacited by 16
  2. 2024TabEBM: A Tabular Data Augmentation Method with Distinct Class-Specific Energy-Based Modelscited by 15
  3. 2025A Survey on Deep Learning Approaches for Tabular Data Generation: Utility, Alignment, Fidelity, Privacy, Diversity, and Beyondcited by 13
  4. 2024Data Plagiarism Index: Characterizing the Privacy Risk of Data-Copying in Tabular Generative Modelscited by 12
  5. 2022Conditional Feature Importance for Mixed Datacited by 7
  6. 2024LaTable: Towards Large Tabular Modelscited by 7
  7. 2024CountARFactuals -- Generating plausible model-agnostic counterfactual explanations with adversarial random forestscited by 5
  8. 2024TabMDA: Tabular Manifold Data Augmentation for Any Classifier using Transformers with In-context Subsettingcited by 5
  9. 2025Privacy Auditing Synthetic Data Release through Local Likelihood Attackscited by 5
  10. 2024Targeted synthetic data generation for tabular data via hardness characterizationcited by 4
  11. 2025Ensembling Membership Inference Attacks Against Tabular Generative Modelscited by 4
  12. 2025Synth-MIA: A Testbed for Auditing Privacy Leakage in Tabular Data Synthesiscited by 4
32 more
  1. 2026Cascaded Flow Matching for Heterogeneous Tabular Data with Mixed-Type Featurescited by 4
  2. 2023CasTGAN: Cascaded Generative Adversarial Network for Realistic Tabular Data Synthesiscited by 3
  3. 2025Autoencoding Random Forestscited by 3
  4. 2025When Tables Leak: Attacking String Memorization in LLM-Based Tabular Data Generationcited by 3
  5. 2025TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformercited by 2
  6. 2025TABFAIRGDT: A Fast Fair Tabular Data Generator using Autoregressive Decision Treescited by 2
  7. 2026TabQueryBench: A Query-Centric Benchmark for Synthetic Tabular Datacited by 2
  8. 2023arfpy: A python package for density estimation and generative modeling with adversarial random forestscited by 1
  9. 2024TAEGAN: Generating Synthetic Tabular Data For Data Augmentationcited by 1
  10. 2024NRGBoost: Energy-Based Generative Boosted Treescited by 1
  11. 2025How Well Does Your Tabular Generator Learn the Structure of Tabular Data?cited by 1
  12. 2025What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Modelscited by 1
  13. 2026xplainfi: Feature Importance and Statistical Inference for Machine Learning in Rcited by 1
  14. 2026A Sobering Look at Tabular Data Generation via Probabilistic Circuitscited by 1
  15. 2026Multimodal synthesis of MRI and tabular data with diffusion in a joint latent space via cross-attentioncited by 1
  16. 2026AugMask: Training Diffusion Models on Incomplete Tabular Data via Stochastic Augmentation and Maskingcited by 1
  17. 2025Disjoint Generation of Synthetic Datacited by 0
  18. 2025Privacy-Preserving Tabular Synthetic Data Generation Using TabularARGNcited by 0
  19. 2025Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forestscited by 0
  20. 2025Cardiac mortality prediction in patients undergoing PCI based on real and synthetic datacited by 0
  21. 2026Probably Approximately Correct Maximum A Posteriori Inferencecited by 0
  22. 2026Machine Learning in Epidemiologycited by 0
  23. 2026How important are the genes to explain the outcome - the asymmetric Shapley value as an honest importance metric for high-dimensional featurescited by 0
  24. 2026Data Augmentation via Causal-Residual Bootstrappingcited by 0
  25. 2026Machine learning models for estimating counterfactuals in a single-arm inflammatory bowel disease studycited by 0
  26. 2026Active Tabular Augmentation via Policy-Guided Diffusion Inpaintingcited by 0
  27. 2026ReMIA: a Powerful and Efficient Alternative to Membership Inference Attacks against Synthetic Data Generatorscited by 0
  28. 2026SoK: Reconstruction Attacks on Synthetic Tabular Data (Insights from Winning the NIST CRC)cited by 0
  29. 2026A Filtered Mixture-of-Generators for Fully Synthetic Survival Trainingcited by 0
  30. 2026Generating Benchmark Health Data Using a Tabular Diffusion Transformercited by 0
  31. 2026Creation begins with understanding: LLMs as strategy designers for privacy-preserving tabular data synthesiscited by 0
  32. 2026Fuzzy Distribution Modeling for Synthetic Tabular Data Generation with Causality Preservationcited by 0