Stable Regret

Stable regret, a measure of an algorithm's performance in online decision-making problems, focuses on minimizing the difference between an algorithm's cumulative reward and the optimal reward achievable with perfect knowledge. Current research explores various algorithms, including proximal point methods, contextual bandits enhanced by large language models, and adaptations of existing algorithms like UCT and Gale-Shapley, to achieve low stable regret in diverse settings such as multi-armed bandits, matching markets, and zero-sum games. These advancements are significant because they improve the efficiency and robustness of online learning systems across numerous applications, from recommendation systems to reinforcement learning. The development of instance-optimal algorithms and the exploration of robust methods against adversarial or delayed feedback are key areas of ongoing investigation.