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

Benjamin Bobbia; Matthias Picard

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arXiv · 2403.15108

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

Benjamin Bobbia·Matthias Picard

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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 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.

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BibTeX RIS

Benjamin Bobbia, Matthias Picard. 2024-03-22. Active Learning for Regression based on Wasserstein distance and GroupSort Neural Networks. https://arxiv.org/abs/2403.15108

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