Sufficient Invariant Learning for Distribution Shift

We identify that existing invariant learning methods are overly conservative—they discard predictive spurious features to ensure invariance. We propose Sufficient Invariant Learning (SIL), which leverages flatness-aware optimization to learn features that are both invariant and maximally predictive across environments, improving out-of-distribution generalization.