Online Learning Algorithm

Online learning algorithms aim to iteratively improve decision-making by adapting to sequentially arriving data, minimizing cumulative error (regret) over time. Current research emphasizes developing algorithms robust to various challenges, including multi-agent interactions, delayed feedback, and noisy or dependent data, often employing techniques like Follow-the-Regularized-Leader (FTRL), Multiplicative Weights Update (MWU), and contextual bandit frameworks. These advancements are significant for diverse applications, from resource allocation and revenue maximization in auctions to personalized recommendations and efficient data processing in resource-constrained environments.