# SPLBoost: An Improved Robust Boosting Algorithm Based on Self-paced Learning

arXiv: https://arxiv.org/abs/1706.06341 · HTML: https://mlsanity.com/paper/1706.06341/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2017.

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

- [ITBoost: Information-Theoretic Trust for Robust Boosting](https://mlsanity.com/paper/2605.04671/) (2026)
- [Robust-GBDT: GBDT with Nonconvex Loss for Tabular Classification in the Presence of Label Noise and Class Imbalance](https://mlsanity.com/paper/2310.05067/) (2023)
- [GAdaBoost: An Efficient and Robust AdaBoost Algorithm Based on Granular-Ball Structure](https://mlsanity.com/paper/2506.02390/) (2025)

## Abstract (arXiv)

It is known that Boosting can be interpreted as a gradient descent technique to minimize an underlying loss function. Specifically, the underlying loss being minimized by the traditional AdaBoost is the exponential loss, which is proved to be very sensitive to random noise/outliers. Therefore, several Boosting algorithms, e.g., LogitBoost and SavageBoost, have been proposed to improve the robustness of AdaBoost by replacing the exponential loss with some designed robust loss functions. In this work, we present a new way to robustify AdaBoost, i.e., incorporating the robust learning idea of Self-paced Learning (SPL) into Boosting framework. Specifically, we design a new robust Boosting algorithm based on SPL regime, i.e., SPLBoost, which can be easily implemented by slightly modifying off-the-shelf Boosting packages. Extensive experiments and a theoretical characterization are also carried out to illustrate the merits of the proposed SPLBoost.