Local Approximation

Local approximation techniques focus on efficiently approximating complex functions or operators using information from a limited neighborhood, aiming to reduce computational cost and improve model interpretability. Current research emphasizes developing improved algorithms for local function approximation within machine learning models, including gradient-based methods and Hessian-based approaches for adaptive learning rates, as well as exploring the use of local linear models for explaining black-box model predictions. These advancements are significant for tackling large-scale problems in various fields, such as Gaussian process modeling, quantum Hamiltonian learning, and the interpretation of complex systems.