mlsanityData to 7 Oct 2026

Neural Random Forests

arXiv 1604.07143 ↗First posted 25 Apr 2016stat.ML cs.LG math.STcited by 25

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Abstract (arXiv)

Given an ensemble of randomized regression trees, it is possible to restructure them as a collection of multilayered neural networks with particular connection weights. Following this principle, we reformulate the random forest method of Breiman (2001) into a neural network setting, and in turn propose two new hybrid procedures that we call neural random forests. Both predictors exploit prior knowledge of regression trees for their architecture, have less parameters to tune than standard networks, and less restrictions on the geometry of the decision boundaries than trees. Consistency results are proved, and substantial numerical evidence is provided on both synthetic and real data sets to assess the excellent performance of our methods in a large variety of prediction problems.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Apr 2016.

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Citing papers per year: 2021: 4; 2022: 7; 2023: 1; 2024: 6; 2025: 3; 2026: 4.

25 papers.

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  1. 2022ExSpliNet: An interpretable and expressive spline-based neural networkcited by 33
  2. 2021Learning Accurate Decision Trees with Bandit Feedback via Quantized Gradient Descentcited by 14
  3. 2024Feature Inference Attack on Shapley Valuescited by 9
  4. 2024Learning a Decision Tree Algorithm with Transformerscited by 6
  5. 2024Vanilla Gradient Descent for Oblique Decision Treescited by 6
  6. 2022Towards a Unified Framework for Uncertainty-aware Nonlinear Variable Selection with Theoretical Guaranteescited by 4
  7. 2021Learning from learning machines: a new generation of AI technology to meet the needs of sciencecited by 3
  8. 2022Towards Adversarially Robust Recommendation from Adaptive Fraudster Detectioncited by 3
  9. 2021Shrub Ensembles for Online Classificationcited by 1
  10. 2022A Hybrid Framework for Sequential Data Prediction with End-to-End Optimizationcited by 1
  11. 2022Sparse tree-based initialization for neural networkscited by 1
  12. 2022To tree or not to tree? Assessing the impact of smoothing the decision boundariescited by 1
13 more
  1. 2025Designing ReLU Generative Networks to Enumerate Trees with a Given Tree Edit Distancecited by 1
  2. 2021Decision Machines: Congruent Decision Treescited by 0
  3. 2022Data-driven multinomial random forest: A new random forest variant with strong consistencycited by 0
  4. 2023Data-driven multinomial random forestcited by 0
  5. 2024A generalized decision tree ensemble based on the NeuralNetworks architecture: Distributed Gradient Boosting Forest (DGBF)cited by 0
  6. 2024Conditional Information Gain Trelliscited by 0
  7. 2024Bayesian Additive Regression Networkscited by 0
  8. 2025Symbolic Branch Networks: Tree-Inherited Neural Models for Interpretable Multiclass Classificationcited by 0
  9. 2025Decision Tree Embedding by Leaf-Meanscited by 0
  10. 2026Neural Network Conversion of Machine Learning Pipelinescited by 0
  11. 2026ReLU Networks for Exact Generation of Similar Graphscited by 0
  12. 2026Approximation-Free Differentiable Oblique Decision Treescited by 0
  13. 2026Theoretical Guarantees for One-Shot Magnitude Pruning and Compute-Adaptive Early Exitcited by 0