Distribution Shift

Distribution shift, the discrepancy between training and deployment data distributions, is a critical challenge in machine learning, hindering model generalization and reliability. Current research focuses on developing methods to detect, adapt to, and mitigate the impact of various shift types (e.g., covariate, concept, label, and performative shifts), employing techniques like data augmentation, model retraining with regularization, and adaptive normalization. These advancements are crucial for improving the robustness and trustworthiness of machine learning models across diverse real-world applications, particularly in safety-critical domains like healthcare and autonomous driving, where unexpected performance degradation can have significant consequences.