The paper introduces Pairwise Difference Learning (PDL) for classification, extending a meta‑learning technique originally designed for regression. PDL learns a function that predicts the difference between outcomes of two instances; predictions for a new instance are obtained by averaging differences with all training examples. Experiments show that the resulting PDL classifier surpasses state‑of‑the‑art methods, and the authors release a user‑friendly Python implementation.
By Mohamed Karim Belaid, Maximilian Rabus, Eyke H\"ullermeier
arXiv:2610.27669v1 Announce Type: cross
Abstract: Deep neural networks often fail at small-sample regression, collapsing to low-variance predictions that miss individual differences. Here, we introdu...
By Robert Bain, Marjan Rashidi, Elizabeth Chrastil
arXiv:2403. 05532v2 Announce Type: replace Abstract: We introduce Tune without Validation (Twin), a simple and effective pipeline for tuning learning rate and weight decay of homogeneous classifiers without validation sets, eliminating the need to hold out data and avoiding the two-step process.
By Lorenzo Brigato, Stavroula Mougiakakou
arXiv:2607. 10391v1 Announce Type: cross Abstract: Despite exposing rich intermediate representations, Vision Transformers (ViTs) are almost exclusively utilized as black-box feature extractors, where only the last layer is considered for downstream tasks.
By Francesco Di Salvo, Shyam Nandan Rai, Hamed Damirchi, Ignacio Meza De la Jara, Sebastian Doerrich, Marco Lents, Christian Ledig
The paper introduces Backward Kernel Herding, an algorithm that iteratively removes data points to create representative subsets for kernel learning, achieving performance comparable to state‑of‑the‑art methods while speeding up subsampling when the reduced size is less than half the original dataset. It also proposes Flexible Kernel Thinning, an extension that allows construction of subsets of any size, not just successive halvings, and demonstrates that this method often yields the best predictive performance. Experiments on Gaussian Processes and Kernel Support Vector Machines show that Backward Kernel Herding excels in training‑time efficiency, while Flexible Kernel Thinning offers superior predictive accuracy and competitive memory usage, emphasizing the need to choose a reduction strategy based on the desired trade‑off between performance, cost, and memory.
By Blanca Cano-Camarero, Yago R. Aguado-Carrillo-de-Albornoz, \'Angela Fern\'andez-Pascual, Jos\'e R. Dorronsoro
The paper refactors and expands the scikit-rebate Python package, adding new Relief‑Based Algorithm (RBA) variants such as SWRF*, mu‑Relief, and five novel methods that use alternative neighbor selection and feature scoring strategies. Benchmarking across diverse genomic simulations shows that most RBAs, except mu‑Relief, effectively detect 2‑way interactions in noisy data, with far‑scoring variants like MultiSWRFDB* excelling at interaction detection but being less sensitive to main effects. The refactored package achieves 10‑ to 35‑fold runtime reductions, and the new RBAs maintain strong performance for both main effects and 2‑way epistatic interactions, preserving predictive signals for downstream modeling.
By Kia Kazemi-Nia, Harsh Bandhey, Philip J. Freda, Ryan J. Urbanowicz