Gam Depth

Generalized Additive Models (GAMs) are a focus of current research aiming to improve the interpretability of machine learning models while maintaining predictive accuracy. This involves developing novel algorithms, such as those leveraging in-context learning for faster and more efficient model fitting, and improving the visual representation of model outputs to reduce cognitive load for users. Research also explores extending GAMs to handle complex data structures like graphs and incorporating semantic information to enhance performance in tasks such as depth estimation and fake news detection. These advancements contribute to the broader goal of creating more trustworthy and understandable AI systems across various applications.