arXiv Machine Learning July 28, 2026 By Silas Koemen

Distributional Split Criteria for Random Forests: Extensions, Shrinkage, and the Robustness of Mean Splitting

Read the original on arXiv Machine Learning →

arXiv:2607. 23721v1 Announce Type: cross Abstract: Distributional random forests replace mean-based CART splitting with criteria that compare the full conditional response distribution in candidate children.

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.

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arXiv Machine Learning

Jun 8

Adaptive Conditional Forest Sampling for Spectral Risk Optimisation under Decision-Dependent Uncertainty

arXiv:2603. 12507v2 Announce Type: replace Abstract: Minimising a spectral risk objective, defined as a weighted combination of expected cost and Conditional Value-at-Risk (CVaR), is challenging when the uncertainty distribution is decision-dependent, making both surrogate modelling and simulation-based ranking sensitive to tail estimation error.

By Marcell T. Kurbucz

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arXiv Machine Learning

Jul 7

Significance-First Splitting: Aligning Treatment Heterogeneity Detection with Honest Estimation

arXiv:2607. 03999v1 Announce Type: cross Abstract: Estimating heterogeneous treatment effects (CATE) requires simultaneously detecting effect modification and quantifying estimation uncertainty.

By Pantelis Z. Hadjipantelis, Josephine Chiang, Karthik Nagesh

arXiv Machine Learning

Jun 18

Kernel of Partition Paths: A Unified Representation for Tree Ensembles

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.

By Nicolas Mahler

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arXiv Machine Learning

Jul 3

Conditional Inference Trees and Forests for Feature Selection

arXiv:2607. 01417v1 Announce Type: new Abstract: Conditional inference trees (CIT) and conditional inference forests (CIF) reduce split-selection bias by testing features before choosing split thresholds, but repeated permutation tests and threshold searches can make these methods computationally expensive.

By Robert Milletich, Justin Downes, Steve Goley, Newel Hirst

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arXiv Statistics ML

Sep 14

Semiparametric Inference for Conditional Shapley Feature Importance

arXiv:2609.10313v1 Announce Type: cross Abstract: Shapley values are widely used for post-hoc feature attribution, but most estimators return point quantities and do not quantify uncertainty, and pop...

By Agostino Gnasso

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arXiv Machine Learning

Aug 12

A Joint-Distribution Route to Fair Representations with Continuous Sensitive Attributes

arXiv:2608. 10470v1 Announce Type: new Abstract: Fair representation learning with a continuous sensitive attribute $S$ requires a representation $Z$ that is statistically independent of $S$.

By Yijin Ni, Xiaoming Huo

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