Dynamic Regret Algorithm

Dynamic regret algorithms address the challenge of online learning in environments where the underlying data distribution changes over time. Current research focuses on developing algorithms with improved regret bounds under various non-stationarity models, including those with unbounded losses or switching preferences, often employing elimination-based rescheduling or adapting existing online learning techniques. These advancements are significant for improving the performance of online learning systems in real-world applications where conditions are inherently dynamic, such as recommendation systems and adaptive control. The development of provably optimal algorithms and matching lower bounds is a key area of ongoing investigation.