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    Deep learning· 2016

    XGBoost: A Scalable Tree Boosting System

    Tianqi Chen (first author) · 2016

    Read the original

    DOI 10.48550/arxiv.1603.02754arXiv 1603.02754OpenAlex W6910681941

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    Why it matters

    Made gradient boosting exceptionally scalable through sparse algorithms, cache awareness, and distributed execution.

    Condensed from the expert lists' notes.

    The record

    First released
    2016 (from arXiv 1603.02754)
    Citations
    63,958 citations, as counted by Papers With Code (CC BY-SA 4.0)
    Named by
    the chatgpt list only
    Also relevant to
    Machine learning foundations
    Score
    0.509 · computed rank 228 · consensus 0.39, must-read 0.5, citations percentile 0.977 (2010s · deep-learning), awards 0, existing lists 0. Method

    Data sources

    Catalogue data from OpenAlex (CC0), Crossref (CC0 metadata; no abstracts are shown), arXiv, and Papers With Code (CC BY-SA 4.0). Each count names its source. See the method.