Active Regression with Adaptive Huber Loss
This addresses regression problems with noisy data, but it is incremental as it builds on existing Huber loss and semi-supervised methods.
The paper tackles scalar regression by optimizing the Huber loss in a semi-supervised setting, achieving robustness to noise with strong performance and low computational cost in diverse applications.
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.