Fast Rate

"Fast rates" in machine learning research refers to the development of algorithms that achieve rapid convergence to optimal solutions, minimizing the computational resources required for training. Current research focuses on improving convergence rates across various learning paradigms, including federated learning, bandit problems, and minimax optimization, often employing techniques like variance reduction, communication compression, and careful exploitation of problem structure (e.g., curvature of feasible sets, smoothness of functions). These advancements are significant because faster algorithms translate to reduced training time and energy consumption, enabling the application of complex models to larger datasets and more challenging problems in diverse fields like reinforcement learning and personalized medicine.