Invariant Risk Minimization Games
This addresses the issue of distribution shift for machine learning practitioners by providing a more stable and efficient algorithm for training invariant predictors, though it is incremental as it builds on existing invariant risk minimization frameworks.
The paper tackles the problem of machine learning models being brittle due to spurious correlations when test distributions differ from training distributions, by proposing a game-theoretic approach to invariant risk minimization that yields similar or better empirical accuracy with much lower variance than prior methods.
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 ensemble game among several environments. By doing so, we develop a simple training algorithm that uses best response dynamics and, in our experiments, yields similar or better empirical accuracy with much lower variance than the challenging bi-level optimization problem of Arjovsky et al. (2019). One key theoretical contribution is showing that the set of Nash equilibria for the proposed game are equivalent to the set of invariant predictors for any finite number of environments, even with nonlinear classifiers and transformations. As a result, our method also retains the generalization guarantees to a large set of environments shown in Arjovsky et al. (2019). The proposed algorithm adds to the collection of successful game-theoretic machine learning algorithms such as generative adversarial networks.