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.

翻译：能够将特征空间与受保护属性解相关是伦理、公平性以及自然科学领域中一个活跃的研究方向。我们提出了一种基于凸神经最优传输求解器（Cnots）的新型解相关方法，该方法能够通过最优传输实现对连续特征空间与受保护属性的解相关。我们展示了该方法在高能物理中喷注分类场景下的优异表现——该场景要求分类器得分与喷注重量解相关。在二分类任务中，该方法实现的解相关水平接近基于条件归一化流的当前最优方法；而在多分类输出场景下，基于最优传输的方法性能显著优于现有最优方法，表明其在多维特征空间解相关方面具有显著优势。