Sufficient Invariant Learning for Distribution Shift

Understand

Learning robust models under distribution shifts between training and test datasets is a fundamental challenge in machine learning.

• While learning invariant features across environments is a popular approach, it often assumes that these features are fully observed in both training and test sets, a condition frequently violated in practice.
• When models rely on invariant features absent in the test set, their robustness in new environments can deteriorate.
• To tackle this problem, we introduce a novel learning principle called the Sufficient Invariant Learning (SIL) framework, which focuses on learning a sufficient subset of invariant features rather than relying on a single feature.