Optimal Baseline

Optimal baselines are crucial for improving the efficiency and accuracy of various machine learning algorithms, particularly in off-policy learning and reinforcement learning, by reducing the variance of estimators. Current research focuses on deriving theoretically optimal baselines, both input-independent and action-dependent, for different learning scenarios, often leveraging control variates or value functions. These advancements lead to improved sample efficiency and reduced computational costs in applications such as recommender systems, policy optimization, and binary classification, ultimately enhancing the reliability and performance of machine learning models.