Concept Shift

Concept shift, the phenomenon where the relationship between input data and output labels changes over time or across different environments, is a critical challenge in machine learning. Current research focuses on developing robust models and algorithms that can adapt to these shifts, employing techniques like concept-aware representation learning, data augmentation, and adaptive weighting strategies within various architectures including neural networks and linear models. Addressing concept shift is crucial for improving the reliability and generalizability of machine learning systems across diverse applications, ranging from time series forecasting and image classification to domain adaptation and automated theory repair. Overcoming this challenge is essential for building more robust and trustworthy AI systems.