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Related Experiment Video

Updated: Feb 28, 2026

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Decorr: Environment partitioning for invariant learning and OOD generalization.

Yufan Liao1, Qi Wu2, Yunjin Wu3

  • 1Institute of Statistics and Big Data, Renmin University of China, Beijing, China; Department of Data Science, City University of Hong Kong, Hong Kong, China.

Neural Networks : the Official Journal of the International Neural Network Society
|February 26, 2026
PubMed
Summary
This summary is machine-generated.

Invariant learning enhances out-of-distribution generalization by finding stable predictors. Our Decorr method improves this by partitioning data into low-correlation environments, boosting performance and applicability.

Keywords:
DecorrelationEnvironment partitioningIRMOOD generalization

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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.