Partial Feedback

Partial feedback, where only incomplete information about outcomes is available, is a central challenge in many machine learning problems, aiming to optimize decision-making with limited observations. Current research focuses on developing algorithms that effectively utilize this partial information, encompassing areas like online learning, reinforcement learning, and Bayesian persuasion, often employing techniques such as confidence bounds, entropy coding, and reward imputation to improve efficiency and accuracy. These advancements have significant implications for various applications, including personalized recommendations, online advertising, and resource allocation, by enabling more efficient and robust learning in scenarios with inherently incomplete data.