# Invariant Risk Minimization Games

@article{Ahuja2020InvariantRM, title={Invariant Risk Minimization Games}, author={Kartik Ahuja and Karthikeyan Shanmugam and Kush Varshney and Amit Dhurandhar}, journal={ArXiv}, year={2020}, volume={abs/2002.04692} }

The standard risk minimization paradigm of machine learning is brittle when operating in environments whose test distributions are different from the training distribution due to spurious correlations. Training on data from many environments and finding invariant predictors reduces the effect of spurious features by concentrating models on features that have a causal relationship with the outcome. In this work, we pose such invariant risk minimization as finding the Nash equilibrium of an… CONTINUE READING

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