Early Stage Convergence

Early stage convergence in machine learning focuses on understanding and improving the initial phases of training algorithms, aiming to accelerate convergence speed and enhance generalization performance. Current research investigates this through the lens of various optimization algorithms (e.g., Adam, SGD, FedProx), model architectures (e.g., transformers, diffusion models), and specific problem domains (e.g., federated learning, collaborative filtering). These studies leverage techniques from dynamical systems theory and optimal transport to establish convergence guarantees and bounds, ultimately contributing to more efficient and robust machine learning systems across diverse applications.