Finite Sample

Finite-sample analysis focuses on understanding the performance of machine learning and statistical algorithms with limited data, aiming to derive rigorous guarantees on their accuracy and efficiency. Current research emphasizes deriving sample complexity bounds for various algorithms, including those used in reinforcement learning, system identification, and causal inference, often employing techniques from information theory and concentration inequalities. These analyses are crucial for ensuring reliable performance in real-world applications where data is often scarce or expensive, and for guiding the development of more efficient and robust algorithms. The resulting theoretical frameworks provide valuable insights into algorithm design and inform the selection of appropriate methods for specific tasks.