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cs.LG2026
Sparse Data Augmentation for Optimization with Provable Guarantees
Behrooz Tahmasebi, Melanie Weber
In nonconvex optimization problems arising in geometric machine learning, data augmentation is commonly used to promote invariance by averaging empirical losses over transformation…
cs.LG2026
Adaptive Symmetry Discovery for Dynamical System Identification
Behrooz Tahmasebi, Melanie Weber
Dynamical systems model trajectory data generated by fixed underlying dynamics, with applications ranging from biology to physics. Especially in scientific settings, dynamical syst…
cs.LG2026
Data Augmentation: A Fourier Analysis Perspective
Behrooz Tahmasebi, Melanie Weber, Stefanie Jegelka
Data augmentation is a simple and model-agnostic approach for exploiting known invariances in learning problems. Given a group acting on the input space, one augments the training…