Optimal Sampling

Optimal sampling aims to select the most informative subset of data for a given task, minimizing data collection costs while maximizing the accuracy of subsequent analyses or model training. Current research focuses on developing algorithms that determine optimal sampling strategies across diverse applications, including generative modeling (e.g., using diffusion models and best-of-n sampling), function approximation (leveraging Christoffel functions), and reinforcement learning for adaptive sampling. These advancements have significant implications for various fields, improving efficiency in areas such as medical imaging, scientific computing, and large language model training by reducing computational burdens and enhancing model performance.