Robust Invariant Representation Learning by Distribution Extrapolation

TL;DR

This paper introduces a new distribution extrapolation framework for invariant risk minimization that improves out-of-distribution generalization by augmenting environmental diversity, outperforming existing IRM methods especially in over-parameterized scenarios.

Contribution

It proposes a novel extrapolation-based approach to enhance IRM robustness by synthetic distributional shifts, addressing limitations of penalty sensitivity.

Findings

Consistently outperforms state-of-the-art IRM variants in diverse experiments

Enhances environmental diversity through synthetic distributional shifts

Demonstrates robustness in over-parameterized and synthetic scenarios

Abstract

Invariant risk minimization (IRM) aims to enable out-of-distribution (OOD) generalization in deep learning by learning invariant representations. As IRM poses an inherently challenging bi-level optimization problem, most existing approaches -- including IRMv1 -- adopt penalty-based single-level approximations. However, empirical studies consistently show that these methods often fail to outperform well-tuned empirical risk minimization (ERM), highlighting the need for more robust IRM implementations. This work theoretically identifies a key limitation common to many IRM variants: their penalty terms are highly sensitive to limited environment diversity and over-parameterization, resulting in performance degradation. To address this issue, a novel extrapolation-based framework is proposed that enhances environmental diversity by augmenting the IRM penalty through synthetic distributional shifts. Extensive experiments -- ranging from synthetic setups to realistic, over-parameterized scenarios -- demonstrate that the proposed method consistently outperforms state-of-the-art IRM variants, validating its effectiveness and robustness.