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XGBoost: A Scalable Tree Boosting System

Tool reference. 71% of classified Pith citations use this work as a method, library, or software dependency, not as a substantive claim.

25 Pith papers citing it
Method reference 71% of classified citations
abstract

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.

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representative citing papers

A Perfect Storm: First-Nature Geography and Economic Development

econ.GN · 2024-08-01 · unverdicted · novelty 7.0

A 1825 storm created a new sea connection in Denmark, producing a 27 percent population increase (elasticity 1.6 to market access) driven by fertility and occupational change toward fishing and manufacturing, with symmetric medieval declines after waterway closure.

A satellite foundation model for improved wealth monitoring

cs.CY · 2026-04-25 · unverdicted · novelty 7.0

Tempov is a self-supervised satellite foundation model that predicts wealth levels and decadal changes at high resolution across Africa from Landsat imagery, outperforming baselines even with limited labels and generalizing temporally.

MuViS: Multimodal Virtual Sensing Benchmark

eess.SP · 2026-03-13 · unverdicted · novelty 6.0

MuViS is a new unified benchmark showing that neither gradient-boosted trees nor deep neural networks hold a universal advantage in multimodal virtual sensing.

Predicting Redshift in Seyfert Galaxies Using Machine Learning

astro-ph.GA · 2026-04-20 · conditional · novelty 4.0

Random Forest regression on combined optical plus mid-infrared colors yields NMAD of 0.0188, R-squared of 0.9561, and 0.294 percent outliers for photometric redshifts in 23,797 Seyfert II galaxies selected from SDSS and WISE.

Exotic Higgs Decays at a Muon Collider

hep-ph · 2026-04-07 · unverdicted · novelty 4.0

Muon colliders at 3 TeV and 10 TeV can probe branching ratios for h to SS decays in 4b and 2b2μ channels down to 10^{-3}–10^{-5}, improving on HL-LHC projections using machine learning.

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Showing 25 of 25 citing papers.