Real Drift

"Drift," in machine learning and related fields, broadly refers to undesirable changes in data distributions or model behavior over time, hindering performance and reliability. Current research focuses on detecting and mitigating drift in various contexts, including federated learning (using algorithms like SCAFFOLD and FedPD to address client heterogeneity), lifelong learning (employing architectures like DriftNet to prevent catastrophic forgetting), and data streams (leveraging uncertainty estimation and online feature monitoring). Understanding and addressing drift is crucial for building robust and reliable AI systems across diverse applications, from autonomous driving to quantum computing and recommender systems.