Active Learning for Regression based on Wasserstein distance and GroupSort Neural Networks
<|reference_start|>Active Learning for Regression based on Wasserstein distance and GroupSort Neural Networks: 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.<|reference_end|>
@article{bobbia2024active,
    title={Active Learning for Regression based on Wasserstein distance and
  GroupSort Neural Networks},
    author={Benjamin Bobbia and Matthias Picard},
    journal={arXiv preprint arXiv:2403.15108},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.15108},
    primaryClass={cs.LG math.ST stat.ML stat.TH}
}