arXiv:2606. 09049v1 Announce Type: cross Abstract: We propose the data augmented bootstrap (DAB), a framework for constructing confidence intervals from approximately invariant transformations of the data.
By Kevin Han Huang
arXiv:2606. 24418v1 Announce Type: new Abstract: Data augmentation is a simple and model-agnostic approach for exploiting known invariances in learning problems.
By Behrooz Tahmasebi, Melanie Weber, Stefanie Jegelka
arXiv:2505. 08784v2 Announce Type: replace-cross Abstract: As machine learning (ML) enters high-stakes domains, trustworthy uncertainty quantification (UQ) is essential for safety.
By Abhineet Agarwal, Fange Xiao, Rebecca Barter, Omer Ronen, Boyu Fan, Bin Yu
arXiv:2606. 31915v1 Announce Type: cross Abstract: While conformal prediction provides a general framework for uncertainty quantification in predictive inference, its application is often limited by computational cost.
By Jiachen Cong, Jingbo Liu
arXiv:2604.22391v2 Announce Type: replace-cross
Abstract: The Super Learner (SL) is a widely used ensemble method that combines point predictions from a library of learners based on their predictive...
By Zhanli Wu, Fabrizio Leisen, Miguel-Angel Luque-Fernandez, F. Javier Rubio
The paper investigates sparse data augmentation for nonconvex optimization in geometric machine learning. It shows that using a small, fixed sample of transformations—obtained before optimization—allows gradient descent to achieve an ε‑stationary point of the fully augmented objective with ≤ O((log|G|+log(1/δ))/ε²) transformation queries. This is more efficient than both full augmentation and standard group‑SGD, which require O(1/ε⁴) queries.