# Condensed Gradient Boosting

arXiv: https://arxiv.org/abs/2211.14599 · HTML: https://mlsanity.com/paper/2211.14599/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 58 references that are in the map.
- First posted on arXiv 26 Nov 2022.

## Builds on (most cited first, 7 of 7)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Tabular Data: Deep Learning is Not All You Need](https://mlsanity.com/paper/2106.03253/) (2021)
- [A Comparative Analysis of XGBoost](https://mlsanity.com/paper/1911.01914/) (2019)
- [GBDT-MO: Gradient Boosted Decision Trees for Multiple Outputs](https://mlsanity.com/paper/1909.04373/) (2019)
- [Compact Multi-Class Boosted Trees](https://mlsanity.com/paper/1710.11547/) (2017)
- [Deep tree-ensembles for multi-output prediction](https://mlsanity.com/paper/2011.02829/) (2020)
- [Multi-target regression via output space quantization](https://mlsanity.com/paper/2003.09896/) (2020)

## Built on by (most cited first, 2 of 2)

- [Robust-Multi-Task Gradient Boosting](https://mlsanity.com/paper/2507.11411/) (2025)
- [Integrated Multivariate Segmentation Tree for Heterogeneous Credit Data Analysis in Small- and Medium-Sized Enterprises](https://mlsanity.com/paper/2509.00550/) (2025)

## Abstract (arXiv)

This paper presents a computationally efficient variant of gradient boosting for multi-class classification and multi-output regression tasks. Standard gradient boosting uses a 1-vs-all strategy for classifications tasks with more than two classes. This strategy translates in that one tree per class and iteration has to be trained. In this work, we propose the use of multi-output regressors as base models to handle the multi-class problem as a single task. In addition, the proposed modification allows the model to learn multi-output regression problems. An extensive comparison with other multi-ouptut based gradient boosting methods is carried out in terms of generalization and computational efficiency. The proposed method showed the best trade-off between generalization ability and training and predictions speeds.