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XGBoost: A Scalable Tree Boosting System

Tool reference. 75% of classified Pith citations use this work as a method, library, or software dependency, not as a substantive claim.

38 Pith papers citing it
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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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A catalogue of TeV pulsar environments

astro-ph.HE · 2026-06-16 · unverdicted · novelty 7.0 · 4 refs

A homogenized catalogue of 128 TeV sources linked to 66 pulsars shows weak correlation between TeV luminosity and pulsar age, indicating environmental effects dominate evolution.

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.

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