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TreeWalker speeds up tree-ensemble inference for grouped data

By PulseAugur Editorial · [1 sources] ·

Researchers have developed TreeWalker, a novel inference technique designed to accelerate the processing of tree-ensemble models when dealing with groups of related data rows. By identifying and treating constant features as static, TreeWalker optimizes traversal by partitioning varying features and skipping irrelevant subtrees. This method, compatible with standard LightGBM and XGBoost models, demonstrated significant speedups, achieving up to 3x faster performance on Intel processors and up to 2.9x on Arm processors compared to row-independent methods for moderate group sizes, with even greater gains at larger group sizes. AI

IMPACT Accelerates inference for common tree-ensemble models, potentially reducing computational costs for applications like survival analysis and click-through-rate prediction.

RANK_REASON Academic paper detailing a new inference technique for tree-ensemble models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

TreeWalker speeds up tree-ensemble inference for grouped data

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Academic paper detailing a new inference technique for tree-ensemble models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Durmus Karatay, Richard Newman ·

    TreeWalker: Partial Evaluation for Grouped Tree-Ensemble Inference

    arXiv:2610.03939v2 Announce Type: replace Abstract: Many prediction tasks evaluate a trained tree ensemble on groups of rows that share feature values: discrete-time survival models expand each patient into $G$ time steps, click-through-rate models score every item in a search se…

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