This paper addresses a new active learning strategy for regression problems. The presented Wasserstein active regression model is based on the principles of distribution-matching to measure the representativeness of the labeled dataset. The Wasserstein distance is computed using GroupSort Neural Networks. The use of such networks provides theoretical foundations giving a way to quantify errors with explicit bounds for their size and depth. This solution is combined with another uncertainty-based approach that is more outlier-tolerant to complete the query strategy. Finally, this method is compared with other classical and recent solutions. The study empirically shows the pertinence of such a representativity-uncertainty approach, which provides good estimation all along the query procedure. Moreover, the Wasserstein active regression often achieves more precise estimations and tends to improve accuracy faster than other models.

本文提出了一种用于回归问题的新型主动学习策略，所提出的Wasserstein主动回归模型基于分布匹配原理来衡量标记数据集的代表性，使用GroupSort神经网络计算Wasserstein距离，该网络提供了理论基础，能够以明确的边界量化错误的大小和深度。该解决方案与另一种基于不确定性的方法相结合，该方法对异常值更具容忍性，以完成查询策略。最后，将该方法与其他经典和最近的解决方案进行对比。实证研究表明，这种代表性-不确定性方法具有一致的良好估计，并且Wasserstein主动回归模型通常能够更精确地估计并且倾向于更快地提高准确性。

基于Wasserstein距离和GroupSort神经网络的回归主动学习