Environment Partitioning For Invariant Learning By Decorrelation

Yufan Liao, Qi WU, Xing YAN

Keywords: OOD Generalization, IRM, Environment Partitioning, Decorrelation

Abstract: Invariant learning methods try to find an invariant predictor across several environments and have become popular in OOD generalization. However, in situations where environments do not naturally exist in the data, they have to be decided by practitioners manually. Environment partitioning, which splits the whole training dataset into environments by algorithms, will significantly influence the performance of invariant learning and has been left undiscussed. A good environment partitioning method can bring invariant learning to applications with more general settings and improve its performance. We propose to split the dataset into several environments by finding low-correlated data subsets. Theoretical interpretations and algorithm details are both introduced in the paper. Through experiments on both synthetic and real data, we show that our Decorr method can achieve outstanding performance, while some other partitioning methods may lead to bad, even below-ERM results using the same training scheme of IRM.

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TL;DR: A method Decorr that does algorithmic environment partitioning and makes IRM more generally applicable.