Correlation Shift

Correlation shift, a type of distribution shift in machine learning, arises when the relationships between features and labels differ between training and testing data. Current research focuses on developing methods to mitigate the negative impact of these shifting correlations on model accuracy and fairness, often employing techniques like disentanglement of features, adjustment of model predictions based on unlabeled data, and the use of causal inference to identify and isolate spurious correlations. Addressing correlation shift is crucial for building robust and reliable machine learning models that generalize well to real-world scenarios, improving the trustworthiness and applicability of AI across various domains.