Optimal Scaling

Optimal scaling investigates how to best adjust parameters within various systems to maximize performance or efficiency. Current research focuses on optimizing scaling in diverse areas, including machine learning model training (e.g., determining optimal model size and training parameters for language models), data visualization (e.g., improving multidimensional projection techniques through user-specific metrics), and sampling algorithms (e.g., finding optimal acceptance rates for Metropolis-Hastings algorithms in discrete spaces). These advancements have significant implications for improving the efficiency and effectiveness of machine learning algorithms, data analysis techniques, and computational methods across numerous fields.