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arXiv:1907.02893stat.ML

Invariant Risk Minimization

Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, David Lopez-Paz

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Abstract

We introduce Invariant Risk Minimization (IRM), a learning paradigm to estimate invariant correlations across multiple training distributions. To achieve this goal, IRM learns a data representation such that the optimal classifier, on top of that data representation, matches for all training distributions. Through theory and experiments, we show how the invariances learned by IRM relate to the causal structures governing the data and enable out-of-distribution generalization.

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Journal reference
DOI
Subjects
stat.MLMachine Learning (Statistics) cs.AIArtificial Intelligence cs.LGMachine Learning

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Linked by 1 identifiable commenter in 1 comment across 1 thread, from Oct 2021 to Oct 2021. Found in 2 HN submissions from Jul 2019 to Mar 2026. Open submission discussions.

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  1. Imitating Deep Learning Dynamics via Stochastic Differential Equations 1 comment

    • This reminded me of Invariant Risk Minimization (IRM) (https://arxiv.org/pdf/1907.02893.pdf) due to the linear bound being sufficient to control the features. Do you have any comments/insight into how you’d say they’re similar/different? Thanks!

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