# Decorrelation using Optimal Transport

arXiv: https://arxiv.org/abs/2307.05187 · HTML: https://mlsanity.com/paper/2307.05187/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 36 references that are in the map.
- First posted on arXiv 11 Jul 2023.

## Builds on (most cited first, 13 of 13)

- [Normalizing Flows for Probabilistic Modeling and Inference](https://mlsanity.com/paper/1912.02762/) (2019)
- [Input Convex Neural Networks](https://mlsanity.com/paper/1609.07152/) (2016)
- [Optimal transport mapping via input convex neural networks](https://mlsanity.com/paper/1908.10962/) (2019)
- [Particle Transformer for Jet Tagging](https://mlsanity.com/paper/2202.03772/) (2022)
- [Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex Optimization](https://mlsanity.com/paper/2012.05942/) (2020)
- [Supervised Training of Conditional Monge Maps](https://mlsanity.com/paper/2206.14262/) (2022)
- [Learning to Pivot with Adversarial Networks](https://mlsanity.com/paper/1611.01046/) (2016)
- [The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider](https://mlsanity.com/paper/2105.14027/) (2021)
- [Decorrelated Jet Substructure Tagging using Adversarial Neural Networks](https://mlsanity.com/paper/1703.03507/) (2017)
- [Transport away your problems: Calibrating stochastic simulations with optimal transport](https://mlsanity.com/paper/2107.08648/) (2021)
- [Flows for Flows: Training Normalizing Flows Between Arbitrary Distributions with Maximum Likelihood Estimation](https://mlsanity.com/paper/2211.02487/) (2022)
- [Preserving physically important variables in optimal event selections: A case study in Higgs physics](https://mlsanity.com/paper/1907.02098/) (2019)
- [Decorrelation with conditional normalizing flows](https://mlsanity.com/paper/2211.02486/) (2022)

## Abstract (arXiv)

Being able to decorrelate a feature space from protected attributes is an area of active research and study in ethics, fairness, and also natural sciences. We introduce a novel decorrelation method using Convex Neural Optimal Transport Solvers (Cnots) that is able to decorrelate a continuous feature space against protected attributes with optimal transport. We demonstrate how well it performs in the context of jet classification in high energy physics, where classifier scores are desired to be decorrelated from the mass of a jet. The decorrelation achieved in binary classification approaches the levels achieved by the state-of-the-art using conditional normalising flows. When moving to multiclass outputs the optimal transport approach performs significantly better than the state-of-the-art, suggesting substantial gains at decorrelating multidimensional feature spaces.