# Gradient Boosted Binary Histogram Ensemble for Large-scale Regression

arXiv: https://arxiv.org/abs/2106.01986 · HTML: https://mlsanity.com/paper/2106.01986/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 40 references that are in the map.
- First posted on arXiv 3 Jun 2021.

## Builds on (most cited first, 5 of 5)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Optimal Learning Rates for Localized SVMs](https://mlsanity.com/paper/1507.06615/) (2015)
- [Patchwork Kriging for Large-scale Gaussian Process Regression](https://mlsanity.com/paper/1701.06655/) (2017)
- [Accelerated Gradient Boosting](https://mlsanity.com/paper/1803.02042/) (2018)
- [Spatial Decompositions for Large Scale SVMs](https://mlsanity.com/paper/1612.00374/) (2016)

## Built on by (most cited first, 1 of 1)

- [Confidence interval estimation of mixed oil length with conditional diffusion model](https://mlsanity.com/paper/2406.18603/) (2024)

## Abstract (arXiv)

In this paper, we propose a gradient boosting algorithm for large-scale regression problems called textitGradient Boosted Binary Histogram Ensemble (GBBHE) based on binary histogram partition and ensemble learning. From the theoretical perspective, by assuming the H"older continuity of the target function, we establish the statistical convergence rate of GBBHE in the space C0,α and C1,0, where a lower bound of the convergence rate for the base learner demonstrates the advantage of boosting. Moreover, in the space C1,0, we prove that the number of iterations to achieve the fast convergence rate can be reduced by using ensemble regressor as the base learner, which improves the computational efficiency. In the experiments, compared with other state-of-the-art algorithms such as gradient boosted regression tree (GBRT), Breiman's forest, and kernel-based methods, our GBBHE algorithm shows promising performance with less running time on large-scale datasets.