Chen and Guestrin: XGBoost

What changed

Tree boosting could be trained at larger scale with explicit algorithmic and systems techniques.

Scientific scope

Exact two-author 2016 paper and its experiments; general boosting theory, later implementations and deployment outcomes excluded.

Evidence

The linked sources identify this contribution and its scope. WRS has not independently reconstructed the scientific claims. The record is the exact two-author 2016 paper and its reported design and evaluations. It does not assign all gradient-boosting theory, later implementations, deployments or universal performance superiority to this work.

Contributors

• Tianqi Chen · SCIENCE: Named source author of the scoped work; individual effort shares are not measured.
• Carlos Guestrin · SCIENCE: Named source author of the scoped work; individual effort shares are not measured.

• Chen and Guestrin, XGBoost: A Scalable Tree Boosting System
• Crossref record for XGBoost

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

Full scientific record · Browse contribution grades · Request a correction