arXiv:2601. 02735v3 Announce Type: replace Abstract: Decision forests induce supervised similarities through the partition structure of their trees.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
arXiv:2510. 09458v2 Announce Type: replace-cross Abstract: Interest in forestry automation is growing alongside rapid advances in deep learning.
The paper presents optimizations for Sparse Oblique (SPO) forests in Google’s Yggdrasil Decision Forests, addressing training speed issues caused by runtime sampling of sparse linear feature combinations. By fixing inefficiencies and introducing two new methods—hierarchical AVX2/AVX-512 vectorized histogram filling and runtime‑dynamic histograms—the authors achieve 2–5× speedups for both Gradient Boosted Trees and Random Forests, bringing SPO‑RF training time on par with axis‑aligned RFs. Extensive evaluation on 19 datasets, including up to 10.5 million rows and 1.6 million features, demonstrates these improvements without compromising accuracy.
arXiv:2606. 18853v1 Announce Type: cross 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.
arXiv:2607. 22763v1 Announce Type: new Abstract: Leaf veins exhibit remarkable diversity in architecture and patterning, yet existing gene--environment association studies have primarily quantified leaf venation using a small collection of low-dimensional summary traits, thereby discarding most of the structural information contained in the original images.
arXiv:2608. 04310v1 Announce Type: new Abstract: The Rashomon effect describes the phenomenon that many models can achieve nearly equivalent performance on the same learning task, with significant ramifications for robustness, feature importance, and customizability.
arXiv:2609.24787v1 Announce Type: new Abstract: Forest inventories increasingly rely on artificial intelligence (AI) models to derive forest attributes from large-scale 3D point clouds. Current model...