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 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：能够在特征空间中去除与受保护属性相关性的能力，是伦理、公平性以及自然科学领域中的活跃研究方向。我们提出了一种新的去相关方法——凸神经最优传输求解器（Cnots），该方法利用最优传输技术实现连续特征空间与受保护属性的去相关。我们展示了该方法在高能物理中喷注分类场景下的优异性能，其中分类器得分需要与喷注重量去相关。在二分类任务中，该方法实现的去相关水平接近于使用条件归一化流的最先进方法。当扩展到多类输出时，基于最优传输的方法显著优于现有最优方法，表明其在多维特征空间去相关方面具有显著优势。