Covariate Shift

Covariate shift describes the problem where the distribution of input features differs between training and testing data, hindering the performance of machine learning models. Current research focuses on mitigating this shift through various techniques, including importance weighting, robust optimization, and the development of invariant representations using methods like conformal prediction and generative models. Addressing covariate shift is crucial for improving the reliability and generalizability of machine learning models across diverse real-world applications, ranging from personalized medicine and autonomous driving to image analysis and causal inference.