Adaptive Regret

Adaptive regret in online learning focuses on minimizing the difference between an algorithm's cumulative loss and that of the best possible strategy in hindsight, but crucially, this comparison is made across all possible sub-intervals of the learning process, not just the entire sequence. Current research emphasizes developing algorithms with computationally efficient adaptive regret bounds, often employing techniques like Follow The Regularized Leader (FTRL), AdaGrad, and various projection-free methods, for diverse settings including online convex optimization, bandits with constraints, and control problems. This research is significant because it provides stronger performance guarantees than traditional regret minimization in non-stationary environments, leading to more robust and adaptable algorithms for applications ranging from resource allocation to online advertising.