Pseudo Regret

Pseudo-regret quantifies the performance of online learning algorithms, particularly in scenarios with limited feedback, by comparing their cumulative reward to that of an optimal strategy with perfect hindsight. Current research focuses on extending pseudo-regret analysis to complex settings like decentralized multi-agent systems and those incorporating fairness constraints, often employing algorithms inspired by multi-armed bandits and linear contextual bandits. These advancements are crucial for improving the efficiency and ethical considerations of algorithms in diverse applications, including online advertising, resource allocation, and hiring processes, where optimizing for both performance and fairness is paramount. The development of algorithms that achieve sublinear pseudo-regret while satisfying constraints like budget limitations or fairness criteria is a major area of ongoing investigation.