Offline Contextual Bandit

Offline contextual bandits address the challenge of learning optimal decision-making policies from pre-collected data, without the ability to actively explore. Current research focuses on developing robust algorithms that handle limited data, confounding variables, and model misspecification, often employing techniques like pessimism, confidence bounds, and convex optimization within linear or neural network models. This field is significant because it enables effective policy learning in scenarios where online exploration is infeasible or costly, with applications ranging from personalized recommendations to resource allocation in complex systems like wireless networks.