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An Empirical Study of Invariant Risk Minimization

Yo Joong Choe, Jiyeon Ham, Kyubyong Park

TL;DR

This study empirically evaluates invariant risk minimization using IRMv1 across extended ColoredMNIST and NLP tasks to understand its practical effectiveness for out-of-distribution generalization. It shows that IRMv1 benefits from diverse training environments and can yield approximately invariant predictors when the true relationship is invariant, with demonstrated applicability to text classification via PunctuatedSST-2. The results reveal robust improvement over ERM under varying spurious correlations, and near-parity with oracle performance in several settings, highlighting IRMv1's potential for robust real-world deployment. The work also provides a ready-to-use codebase and discusses directions for scaling IRM through more stable approximations and richer multi-environment setups, aiming to broaden IRM's applicability.

Abstract

Invariant risk minimization (IRM) (Arjovsky et al., 2019) is a recently proposed framework designed for learning predictors that are invariant to spurious correlations across different training environments. Yet, despite its theoretical justifications, IRM has not been extensively tested across various settings. In an attempt to gain a better understanding of the framework, we empirically investigate several research questions using IRMv1, which is the first practical algorithm proposed to approximately solve IRM. By extending the ColoredMNIST experiment in different ways, we find that IRMv1 (i) performs better as the spurious correlation varies more widely between training environments, (ii) learns an approximately invariant predictor when the underlying relationship is approximately invariant, and (iii) can be extended to an analogous setting for text classification.

An Empirical Study of Invariant Risk Minimization

TL;DR

This study empirically evaluates invariant risk minimization using IRMv1 across extended ColoredMNIST and NLP tasks to understand its practical effectiveness for out-of-distribution generalization. It shows that IRMv1 benefits from diverse training environments and can yield approximately invariant predictors when the true relationship is invariant, with demonstrated applicability to text classification via PunctuatedSST-2. The results reveal robust improvement over ERM under varying spurious correlations, and near-parity with oracle performance in several settings, highlighting IRMv1's potential for robust real-world deployment. The work also provides a ready-to-use codebase and discusses directions for scaling IRM through more stable approximations and richer multi-environment setups, aiming to broaden IRM's applicability.

Abstract

Invariant risk minimization (IRM) (Arjovsky et al., 2019) is a recently proposed framework designed for learning predictors that are invariant to spurious correlations across different training environments. Yet, despite its theoretical justifications, IRM has not been extensively tested across various settings. In an attempt to gain a better understanding of the framework, we empirically investigate several research questions using IRMv1, which is the first practical algorithm proposed to approximately solve IRM. By extending the ColoredMNIST experiment in different ways, we find that IRMv1 (i) performs better as the spurious correlation varies more widely between training environments, (ii) learns an approximately invariant predictor when the underlying relationship is approximately invariant, and (iii) can be extended to an analogous setting for text classification.

Paper Structure

This paper contains 17 sections, 2 equations, 2 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Background: Invariant Risk Minimization
  3. The IRM Framework
  4. The IRMv1 Algorithm
  5. Extended ColoredMNIST
  6. How does the diversity of training environments affect IRMv1's performance?
  7. How does approximate invariance affect IRMv1's performance?
  8. PunctuatedSST-2
  9. Can IRMv1 work with spurious token-to-label correlations in text classification tasks?
  10. Conclusion and Future Work
  11. ColoredMNIST is a Special Case of Extended ColoredMNIST
  12. Extended ColoredMNIST Training Details
  13. Additional Extended ColoredMNIST Experiments
  14. Is IRMv1 still effective when the invariant correlation is stronger than the spurious one?
  15. Does IRMv1 work with more than two training environments?
  16. ...and 2 more sections

Figures (2)

  • Figure 1: Accuracy on Extended ColoredMNIST, train (left) and test (right), versus the difference in spurious correlations between the two training environments, $|p_1 - p_2|$. Averaged over 10 trials (error bars represent standard deviations).
  • Figure 2: Accuracy on Extended ColoredMNIST, train (left) and test (right), versus the gap in label corruption ratio across training environments, $|\eta_1 - \eta_2|$. Averaged over 10 trials (error bars represent standard deviations).