Active Learning for Regression based on Wasserstein distance and
  GroupSort Neural Networks
Benjamin Bobbia Matthias Picard
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.