The Matching Principle: A Geometric Theory of Loss Functions for Nuisance-Robust Representation Learning 事件

The Matching Principle: A Geometric Theory of Loss Functions for Nuisance-Robust Representation Learning arXiv:2605.22800v2 Announce Type: replace-cross Abstract: Robustness, domain adaptation, photometric/occlusion invariance, sensor drift, and alignment style are treated as separate literatures with separate method families. Under label-preserving deployment shift they share one geometric object: the covariance Sigma_task = Cov_{Q_n}(n) of ways inputs can change without changing the label. CO