arXiv:2607. 11956v1 Announce Type: cross Abstract: Data Shapley is the standard principled answer to which training points are worth what, and its k-nearest-neighbor (KNN) specialization is the version deployed in practice: the exact estimator shipped by toolkits such as pyDVL and OpenDataVal.
By Zongye Lyu
arXiv:2606. 29403v1 Announce Type: cross Abstract: Conformal prediction guarantees marginal coverage, but pooled calibration averages over heterogeneous regions and can mask regional undercoverage in safety-critical subgroups.
By Louis Berthier, Ahmed Shokry, Maxime Moreaud, Guillaume Ramelet, Aymeric Dieuleveut
arXiv:2607. 21003v1 Announce Type: new Abstract: Ordinal Classification (OC) deals with classification tasks where the classes follow a natural order.
By Rafael Ayll\'on-Gavil\'an, Francisco Jos\'e Mart\'inez-Estudillo, David Guijo-Rubio, C\'esar Herv\'as-Mart\'inez, Pedro A. Guti\'errez
arXiv:2605. 20716v5 Announce Type: replace Abstract: Random forests construct each tree with a different, randomised representation of the feature space.
By Youngjoon Park
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
arXiv:2606. 27997v1 Announce Type: new Abstract: Benchmarks of machine learning models often include many datasets, making evaluation expensive.
By Rostislav Gusev, Alexey Zaytsev