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Elyssa Hofgard

2 accepted papers

2026

To Augment or Not to Augment? Diagnosing Distributional Symmetry Breaking

ICLR 2026poster

Symmetry-aware methods for machine learning, such as data augmentation and equivariant architectures, encourage correct model behavior on all transformations (e.g. rotations or permutations) of the original dataset. These methods can impart improved generalization and sample efficiency, under the as…

Cited by 0SourceScholar
2024

Discovering Symmetry Breaking in Physical Systems with Relaxed Group Convolution

ICML 2024poster

Modeling symmetry breaking is essential for understanding the fundamental changes in the behaviors and properties of physical systems, from microscopic particle interactions to macroscopic phenomena like fluid dynamics and cosmic structures. Thus, identifying sources of asymmetry is an important too…

Cited by 7SourcePDFScholar