arXiv:2601. 02735v3 Announce Type: replace Abstract: Decision forests induce supervised similarities through the partition structure of their trees.
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...
arXiv:2503. 12902v4 Announce Type: replace Abstract: Model trees provide an appealing way to perform interpretable machine learning for both classification and regression problems.
arXiv:2603. 23669v2 Announce Type: replace-cross Abstract: Accurate estimation of forest biomass, a major carbon sink, relies heavily on tree-level traits such as height and species.
arXiv:2606. 08206v1 Announce Type: cross Abstract: We present SegmentAnyTreeV2, a sensor- and platform-agnostic framework for semantic and instance segmentation of forest point clouds.
MotherTree is a tabular transformer that meta‑learns to produce hard, axis‑aligned decision trees in a single forward pass. It is pre‑trained on a synthetic prior distribution using stochastic gradient descent, without needing reference trees for supervision. On controlled benchmarks, MotherTree matches or surpasses size‑matched trees from traditional algorithms and serves as a strong initializer for task‑specific fine‑tuning.
ForestQuery is a new framework for unified forest point cloud segmentation that incorporates boundary-aware and spatially anchored query learning. It explicitly models boundary uncertainty to improve instance query construction and uses learnable 3D anchors to encode forest vertical stratification for semantic queries. Experiments on public benchmarks and a real‑world dataset show consistent gains in both individual‑tree and semantic segmentation.
arXiv:2608.24104v1 Announce Type: cross Abstract: Numerous methods have been developed to quantify feature attributions in individual predictions for tree ensembles. However, many applications requir...