论文标题
对不变风险最小化的实证研究
An Empirical Study of Invariant Risk Minimization
论文作者
论文摘要
不变风险最小化（IRM）（Arjovsky等人，2019年）是一个最近提出的框架，旨在学习预测因子，这些预测因素与不同培训环境的虚假相关性不变。然而，尽管有理论上的理由，但IRM并未在各种环境中进行广泛的测试。为了更好地了解该框架，我们使用IRMV1进行了验证研究，这是第一种实用算法，该算法大致解决了IRM。通过以不同的方式扩展有色人种的实验，我们发现IRMV1（i）的性能更好，因为培训环境之间伪造的相关性变化更大，（ii）在近似不变的情况下，学会了大约不变的预测因子，并且可以将（iii）扩展到用于文本分类的类似过程中。
Invariant risk minimization (IRM) (Arjovsky et al., 2019) is a recently proposed framework designed for learning predictors that are invariant to spurious correlations across different training environments. Yet, despite its theoretical justifications, IRM has not been extensively tested across various settings. In an attempt to gain a better understanding of the framework, we empirically investigate several research questions using IRMv1, which is the first practical algorithm proposed to approximately solve IRM. By extending the ColoredMNIST experiment in different ways, we find that IRMv1 (i) performs better as the spurious correlation varies more widely between training environments, (ii) learns an approximately invariant predictor when the underlying relationship is approximately invariant, and (iii) can be extended to an analogous setting for text classification.
