  This paper addresses the scalar regression problem through a novel solution
to exactly optimize the Huber loss in a general semi-supervised setting, which
combines multi-view learning and manifold regularization. We propose a
principled algorithm to 1) avoid computationally expensive iterative schemes
while 2) adapting the Huber loss threshold in a data-driven fashion and 3)
actively balancing the use of labelled data to remove noisy or inconsistent
annotations at the training stage. In a wide experimental evaluation, dealing
with diverse applications, we assess the superiority of our paradigm which is
able to combine robustness towards noise with both strong performance and low
computational cost.
