AWhat changed
Combined gradient-based one-side sampling with reweighting of small-gradient samples and greedy bundling of sparse, nearly exclusive features to reduce histogram-based tree-training cost.
Scientific scope
Combined gradient-based one-side sampling with reweighting of small-gradient samples and greedy bundling of sparse, nearly exclusive features to reduce histogram-based tree-training cost. Inherits gradient boosting and histogram trees. GOSS approximates split gains under sampling/size conditions; EFB is lossless only for suitable exclusivity, with conflicts trading accuracy for speed. No unconditional constant-factor graph-coloring/bundling theorem or universal20xperformance claim exported from the paper's phrasing.2017paper differs from2016project establishment.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Inherits gradient boosting and histogram trees. GOSS approximates split gains under sampling/size conditions; EFB is lossless only for suitable exclusivity, with conflicts trading accuracy for speed. No unconditional constant-factor graph-coloring/bundling theorem or universal20xperformance claim exported from the paper's phrasing.2017paper differs from2016project establishment.
Contributors
- Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
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