Convex Model

Convex models are increasingly used in machine learning to address challenges in areas like data privacy, decentralized learning, and feature selection, offering advantages over their non-convex counterparts in terms of computational efficiency and theoretical guarantees. Current research focuses on developing and analyzing algorithms for convex optimization within various settings, including federated learning and sparse principal component analysis, often incorporating techniques like natural gradient descent and momentum-based methods. This work is significant because it provides more robust and interpretable solutions to complex machine learning problems, leading to improved model performance and enhanced privacy protections.