Pas Stochastic Gradient Descent

Pas (multi-pass) Stochastic Gradient Descent (SGD) investigates the effects of repeatedly using the same data batches during training, contrasting it with single-pass SGD. Current research focuses on understanding how this reuse impacts learning dynamics, generalization performance, and the efficiency of optimization, particularly in high-dimensional settings and for specific model architectures like two-layer neural networks. These studies aim to clarify the interplay between optimization and generalization in pas SGD, providing a more nuanced understanding of its strengths and limitations compared to single-pass SGD and full-batch gradient descent. This work has implications for both theoretical understanding of optimization algorithms and practical improvements in training efficiency and generalization capabilities of machine learning models.