# Reduced Robust Random Cut Forest for Out-Of-Distribution detection in machine learning models

arXiv: https://arxiv.org/abs/2206.09247 · HTML: https://mlsanity.com/paper/2206.09247/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 24 references that are in the map.
- First posted on arXiv 18 Jun 2022.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [Concrete Problems in AI Safety](https://mlsanity.com/paper/1606.06565/) (2016)
- [Real-time Out-of-distribution Detection in Learning-Enabled Cyber-Physical Systems](https://mlsanity.com/paper/2001.10494/) (2020)
- [Out-of-Distribution Detection in Multi-Label Datasets using Latent Space of β-VAE](https://mlsanity.com/paper/2003.08740/) (2020)
- [Rare event failure test case generation in Learning-Enabled-Controllers](https://mlsanity.com/paper/2206.05533/) (2022)
- [Random Forests for Big Data](https://mlsanity.com/paper/1511.08327/) (2015)
- [Sample-efficient Deep Reinforcement Learning for Dialog Control](https://mlsanity.com/paper/1612.06000/) (2016)

## Built on by (most cited first, 7 of 7)

- [Constrained Bayesian Optimization for Automatic Underwater Vehicle Hull Design](https://mlsanity.com/paper/2302.14732/) (2023)
- [Search for universal minimum drag resistance underwater vehicle hull using CFD](https://mlsanity.com/paper/2302.09441/) (2023)
- [Fusion of ML with numerical simulation for optimized propeller design](https://mlsanity.com/paper/2302.14740/) (2023)
- [Vehicle lateral control using Machine Learning for automated vehicle guidance](https://mlsanity.com/paper/2303.08187/) (2023)
- [Automatic AI controller that can drive with confidence: steering vehicle with uncertainty knowledge](https://mlsanity.com/paper/2404.16893/) (2024)
- [Systematic design space exploration by learning the explored space using Machine Learning](https://mlsanity.com/paper/2303.08249/) (2023)
- [Optimization for truss design using Bayesian optimization](https://mlsanity.com/paper/2306.01763/) (2023)

## Abstract (arXiv)

Most machine learning-based regressors extract information from data collected via past observations of limited length to make predictions in the future. Consequently, when input to these trained models is data with significantly different statistical properties from data used for training, there is no guarantee of accurate prediction. Consequently, using these models on out-of-distribution input data may result in a completely different predicted outcome from the desired one, which is not only erroneous but can also be hazardous in some cases. Successful deployment of these machine learning models in any system requires a detection system, which should be able to distinguish between out-of-distribution and in-distribution data (i.e. similar to training data). In this paper, we introduce a novel approach for this detection process using a Reduced Robust Random Cut Forest (RRRCF) data structure, which can be used on both small and large data sets. Similar to the Robust Random Cut Forest (RRCF), RRRCF is a structured, but a reduced representation of the training data sub-space in form of cut trees. Empirical results of this method on both low and high-dimensional data showed that inference about data being in/out of training distribution can be made efficiently and the model is easy to train with no difficult hyper-parameter tuning. The paper discusses two different use-cases for testing and validating results.