Function Decomposition

Function decomposition aims to break down complex systems or models into simpler, more interpretable components, facilitating analysis and understanding. Current research focuses on applying this principle to diverse areas, including machine learning model explainability (using techniques like additive models and orthogonalization), code generation (via recursive function branching and consensus-based approaches), and time series analysis (employing Bayesian methods and functional data analysis). These advancements enhance the interpretability of black-box models, improve code generation efficiency, and enable more robust handling of complex data, impacting fields ranging from materials science to healthcare.