2026
Representation Learning for Equivariant Inference with Guarantees
Daniel Felipe Ordonez Apraez, Vladimir Kostic, Alek Fröhlich, Vivien Brandt, Karim Lounici, Massimiliano Pontil
ICML 2026poster
In many real-world applications of regression, conditional probability estimation, and uncertainty quantification, exploiting symmetries rooted in physics or geometry can dramatically improve generalization and sample efficiency. While geometric deep learning has made empirical advances by incorpora…