What is invariant risk minimization (IRM) evaluation?

IRM evaluates models that learn stable predictive features across environments.

Invariant Risk Minimization aims to learn representations that perform well across multiple environments by enforcing invariance in optimal classifiers. Evaluation checks whether performance remains stable across distribution shifts. This is critical in domain generalization, where models must generalize beyond training environments.