Active Learning for Regression based on Wasserstein distance and GroupSort Neural Networks

arXiv:2403.15108v1 Announce Type: new
Abstract: 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 …

abstract active learning arxiv cs.lg dataset distribution math.st networks neural networks paper regression stat.ml stat.th strategy type