High Dimensional Clipping

High-dimensional clipping is a technique used in machine learning to stabilize training and improve the robustness of optimization algorithms, particularly when dealing with large models and noisy data. Current research focuses on understanding the theoretical properties of clipping within various optimization methods (e.g., SGD, Adam, PPO) and its impact on model architectures like Vision Transformers and Convolutional Neural Networks, as well as its application in differentially private training and federated learning. This work aims to improve the efficiency and effectiveness of training, enhance model generalization, and address challenges posed by high dimensionality and noisy data, ultimately leading to more reliable and performant machine learning systems.