Sample Complexity

Sample complexity, the number of data points needed for accurate learning, is a central problem in machine learning and related fields. Current research focuses on improving sample efficiency across various learning paradigms, including reinforcement learning (with algorithms like policy gradient methods and Q-learning variants), imitation learning, and distributionally robust optimization, often employing techniques like variance reduction and function approximation. These advancements are crucial for scaling machine learning algorithms to larger datasets and higher-dimensional problems, impacting fields ranging from robotics and control systems to statistical inference and quantum computing. A key trend is the development of algorithms with theoretically guaranteed sample complexity bounds, moving beyond empirical observations to provide rigorous performance guarantees.