Invariant Risk Minimization

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.
ARXIV: [1907.02893v1](http://arxiv.org/abs/1907.02893v1)