Confidence Bound

Confidence bounds quantify the uncertainty in estimates, a crucial aspect in various fields from machine learning to statistical inference. Current research focuses on developing tighter and more efficient confidence bounds within specific algorithms like Upper Confidence Bound (UCB) for multi-armed bandits and its variants, Bayesian methods, and Gaussian processes, often addressing challenges like non-stationarity, delayed feedback, and high dimensionality. These advancements improve the reliability and efficiency of decision-making in applications ranging from online advertising and recommender systems to autonomous systems and clinical trials, ultimately leading to more robust and trustworthy algorithms.