Sparse Data Augmentation for Optimization with Provable Guarantees
In nonconvex optimization problems arising in geometric machine learning, data augmentation is commonly used to promote invariance by averaging empirical losses over transformations of the data. Computing the fully augmented objective, however, requires access to every element of the transformation group $G$, which may be prohibitively expensive when $G$ is large or accessible only through sampling. We study whether full augmentation can instead be approximated using a small, fixed sample of transformations acquired before optimization and reused thereafter. Under suitable regularity conditions, we show that, with probability at least $1-δ$, gradient descent (GD) on the resulting sparsely...
| Authors | Behrooz Tahmasebi, Melanie Weber |
|---|---|
| Primary category | cs.LG |
| Published | 8 September 2026 |
| Last updated at source | 16 September 2026 |
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