Future applications of national importance, such as healthcare, critical infrastructure, transportation systems, and smart cities, are expected to increasingly rely on machine-learning methods, ...

Significant progress has been made in machine learning with large amounts of clean labels and static data. However, in many real-world applications, the data often changes with time and it is ...

Real-world data typically exhibits long-tailed class distribution and contains label noise. Previous long-tail learning ...