# Active Regression with Adaptive Huber Loss

arXiv: https://arxiv.org/abs/1606.01568 · HTML: https://mlsanity.com/paper/1606.01568/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2016.

## Built on by (most cited first, 3 of 3)

- [Distortion-Tolerant Monocular Depth Estimation On Omnidirectional Images Using Dual-cubemap](https://mlsanity.com/paper/2203.09733/) (2022)
- [Using Robust Regression to Find Font Usage Trends](https://mlsanity.com/paper/2106.15232/) (2021)
- [Residual-based Adaptive Huber Loss (RAHL) -- Design of an improved Huber loss for CQI prediction in 5G networks](https://mlsanity.com/paper/2408.14718/) (2024)

## Abstract (arXiv)

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.