Continual Invariant Risk Minimization
This work addresses the challenge of maintaining invariant representations in continual learning settings, which is incremental as it adapts an existing method to sequential environments.
The authors tackled the problem of learning invariant feature representations when environments are observed sequentially, rather than simultaneously, by extending invariant risk minimization (IRM) to continual learning scenarios. They developed a variational Bayesian and bilevel framework, showing empirically that their methods outperform or are competitive with prior approaches on multiple datasets.
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 for discovering environment-invariant representations. IRM was introduced by Arjovsky et al. (2019) and extended by Ahuja et al. (2020). IRM assumes that all environments are available to the learning system at the same time. With this work, we generalize the concept of IRM to scenarios where environments are observed sequentially. We show that existing approaches, including those designed for continual learning, fail to identify the invariant features and models across sequentially presented environments. We extend IRM under a variational Bayesian and bilevel framework, creating a general approach to continual invariant risk minimization. We also describe a strategy to solve the optimization problems using a variant of the alternating direction method of multiplier (ADMM). We show empirically using multiple datasets and with multiple sequential environments that the proposed methods outperform or is competitive with prior approaches.