Invariant Risk Minimization
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.
Code (18)
Tasks
Domain GeneralizationImage ClassificationOut-of-Distribution GeneralizationSimilar Papers 제목 키워드 기반
Continual Invariant Risk Minimization
Empirical risk minimization can lead to poor generalization behavior on unseen environments if the learned model does not capture invariant feature representations. Invariant risk minimization (IRM) is a recent proposal …
Continual LearningKernelized Heterogeneous Risk Minimization
The ability to generalize under distributional shifts is essential to reliable machine learning, while models optimized with empirical risk minimization usually fail on non-$i.i.d$ testing data. Recently, invariant learn…
The Risks and Rewards of Invariant Risk Minimization
Spurious correlations are one of the most prominent pain points for building and deploying machine learning models. Invariant Risk Minimization (IRM) is a learning algorithm designed to mitigate the effect of spurious fe…
BIG-bench Machine LearningBayesian Invariant Risk Minimization
Generalization under distributional shift is an open challenge for machine learning. Invariant Risk Minimization (IRM) is a promising framework to tackle this issue by extracting invariant features. However, despite …
Bayesian InferenceHeterogeneous Risk Minimization
Machine learning algorithms with empirical risk minimization usually suffer from poor generalization performance due to the greedy exploitation of correlations among the training data, which are not stable under distribu…