arXiv:2606. 24418v1 Announce Type: new Abstract: Data augmentation is a simple and model-agnostic approach for exploiting known invariances in learning problems.
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.
arXiv:2609.08133v1 Announce Type: cross Abstract: In nonconvex optimization problems arising in geometric machine learning, data augmentation is commonly used to promote invariance by averaging empir...
arXiv:2609.07031v1 Announce Type: new Abstract: Learning with group invariances is central to many scientific and geometric learning problems, yet its computational foundations remain poorly understo...
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.
The paper investigates self‑supervised pre‑training that uses multiple data augmentations of the same unlabeled sample. It shows that pooling these dependent augmentations together yields statistical estimation error bounds that are never worse than, and sometimes better than, partitioning the data into independent subsets. The analysis explains why using many augmentations is practically advantageous, especially when their correlations have mild effects or reduce estimation variance.
arXiv:2604. 00316v2 Announce Type: replace-cross Abstract: Grokking occurs when a model achieves high training accuracy but generalization to unseen test points happens long after that.
arXiv:2511. 09432v2 Announce Type: replace Abstract: Machine learning (ML) models achieve remarkable performance but remain hard to interpret due to their scale and complexity.