Out-of-Distribution Generalization Analysis via Influence Function

Understand

The mismatch between training and target data is one major challenge for current machine learning systems.

• When training data is collected from multiple domains and the target domains include all training domains and other new domains, we are facing an Out-of-Distribution (OOD) generalization problem that aims to find a model with the best OOD accuracy.
• One of the definitions of OOD accuracy is worst-domain accuracy.
• In general, the set of target domains is unknown, and the worst over target domains may be unseen when the number of observed domains is limited.