mlsanityData to 14 Oct 2026

XGBoost: A Scalable Tree Boosting System

arXiv 1603.02754 ↗First posted 9 Mar 2016cs.LGcited by 5,166

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Cited by 73 in LLM agents and tool use, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges. We propose a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning. More importantly, we provide insights on cache access patterns, data compression and sharding to build a scalable tree boosting system. By combining these insights, XGBoost scales beyond billions of examples using far fewer resources than existing systems.

Key facts. Cited by 5,166 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 Mar 2016.

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Citing papers per year: 2021: 529; 2022: 598; 2023: 742; 2024: 951; 2025: 1131; 2026: 1215.

5,166 papers.

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