arXiv:2609. 03129v1 Announce Type: cross Abstract: Several classical machine-learning methods, such as KRRs and SVRs, are both computationally and analytically tractable since their estimators either admit closed-form expressions or are obtained by minimizing convex training objectives; neither feature is generally available for deep neural networks.
By Ruiyang Hong, Hrad Ghoukasian, Anastasis Kratsios
The paper studies algorithms for computing the Entropic Gromov-Wasserstein (EGW) distance, a measure of discrepancy between metric measure spaces. It introduces Averaged Mirror Descent (AMD), which averages successive Mirror Descent steps and is proven to converge for any cost function, and shows that a dual gradient method with a fixed step size also converges for arbitrary costs, even when iterations are inexact. Empirical comparisons demonstrate that both AMD and the dual gradient method succeed on cases where classical Mirror Descent fails.
By Joanna Marks, Gabriel Rioux, Riccardo Passeggeri
The paper introduces Kernelized Parameter Sensitivity Atlases (KPSA), a method that uses a block decomposition of the empirical neural tangent kernel to estimate input‑specific parameter sensitivities efficiently. KPSA embeds parameter groups in a query space and applies a lightweight kernel to predict sensitivities for unseen inputs, achieving a high fraction of the perturbation susceptibility of full back‑propagation while dramatically reducing computation and memory usage. Experiments across vision, forecasting, and protein‑modeling tasks show that KPSA identifies sparse, depth‑spanning sensitivity circuits and improves throughput and memory efficiency in activation‑attribution problems.
By Davide Wiest, Ahanaf Hasan Ariq, Abdul Monaf Chowdhury
arXiv:2602. 00329v4 Announce Type: replace-cross Abstract: Reliable data attribution is essential for mitigating bias and reducing computational waste in modern machine learning, with the Shapley value serving as the theoretical gold standard.
By Meng Ding, Zeqing Zhang, Di Wang, Lijie Hu
arXiv:2606. 19411v1 Announce Type: new Abstract: Selecting a small, diverse, high-quality subset from a massive pool of candidates is a recurring primitive in modern machine learning -- data curation and coreset selection for training and fine-tuning large models, active-learning batch acquisition, prompt and exemplar selection for in-context learning, retrieval diversification, and experimental design.
By Richard Yi Da Xu
arXiv:2609.27988v1 Announce Type: cross
Abstract: Methods operating on Vision Transformer (ViT) feature spaces typically rely on Euclidean distance or cosine similarity. This assumes that every direc...
By Andrew Bond, Ege Erdem \"Ozl\"u, Tuna \c{C}imen, Ilkin Umut Melanlioglu, Tolga Birdal, Erkut Erdem, Aykut Erdem