Drift Estimator

Drift estimation focuses on detecting and characterizing deviations in data streams over time, crucial for maintaining the reliability of systems ranging from autonomous vehicles to machine learning models. Current research emphasizes developing robust and efficient drift estimators, exploring techniques like ensemble methods and incorporating physical models of data generation to improve accuracy and interpretability, particularly in resource-constrained environments like IoT networks. These advancements are vital for enhancing the robustness and reliability of various applications, from ensuring the safety of autonomous systems to improving the performance and trustworthiness of machine learning models in real-world deployments.