Decorr: Environment partitioning for invariant learning and OOD generalization

Area of Science:

• Machine Learning
• Artificial Intelligence
• Data Science

Background:

• Invariant learning methods are crucial for out-of-distribution (OOD) generalization.
• Manual environment partitioning is often required but impacts method efficacy.
• Effective partitioning strategies are under-explored in the literature.

Purpose of the Study:

• To introduce a novel method for environment partitioning to improve invariant learning.
• To enhance the performance and applicability of invariant learning techniques.
• To address the challenge of spurious correlations in OOD generalization.

Main Methods:

• Proposed the Decorr method for partitioning datasets into low-correlation environments.
• Applied Decorr in conjunction with existing invariant learning algorithms.
• Evaluated performance using both synthetic and real-world datasets.

Main Results:

• The Decorr method demonstrated superior performance when combined with invariant learning.
• Decorr effectively mitigates spurious correlations, identifying more stable predictors.
• The approach broadens the applicability of invariant learning methods.

Conclusions:

• Environment partitioning is a critical, yet under-discussed, component of invariant learning.
• The Decorr method offers a principled way to partition data for improved OOD generalization.
• This work facilitates wider adoption and enhanced effectiveness of invariant learning.