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Kernel of Partition Paths: A Unified Representation for Tree Ensembles

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arXiv:2606. 18853v1 Announce Type: new Abstract: A recent line of work has reframed individual decision trees as linear models on engineered features associated with their splits, opening routes for oracle inequalities and feature-importance reinterpretation, but leaving open the question of what unified geometric object a forest induces when one indexes its feature map by nodes rather than by splits.

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Authors: Nicolas Mahler
Released: 2026-06-18 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because Kernel of Partition Paths: A Unified Representation for Tree Ensembles overlaps with clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)", clean result "A pretraining-data-poisoned Qwen3-4B backdoor only fires on the exact trigger tokens — paraphrases don't activate it, and base-model similarity to the trigger doesn't predict which inputs fire (MODERATE confidence)". Matching terms: class, under, line, rate, model. Source: arxiv stat.ML (Machine Learning).
arXiv: 2606.18853

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