Hugging Face Trending Papers August 4, 2026

Double Descent in Gradient Boosting Decision Trees via Split-Candidate Scaling

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Double descent is commonly studied by scaling an explicit capacity parameter, such as neural-network width. For gradient boosting decision trees (GBDTs), however, an analogous single-axis capacity parameter has not been established.

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efficiency

arXiv Machine Learning

Aug 5

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efficiency

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benchmarks safety

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efficiency benchmarks

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benchmarks

arXiv Machine Learning

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safety

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benchmarks safety

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Double Descent in Gradient Boosting Decision Trees via Split-Candidate Scaling

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