Substitute Model

Substitute models are computational representations that replace complex or unavailable components in various systems, aiming to improve efficiency, transferability, or interpretability. Current research focuses on developing and evaluating substitute models across diverse applications, including distributional regression, recommendation systems, adversarial attacks on deep neural networks, and expert aggregation, employing techniques like inverse conditional flows, transformers, and Bayesian methods. These advancements have implications for improving the robustness and explainability of machine learning models, enhancing personalized recommendations in e-commerce, and advancing our understanding of complex systems through simplified, yet effective, representations.