Noisy Gradient Descent

Noisy gradient descent is a technique used to train machine learning models, particularly neural networks, by adding noise to the gradient updates during optimization. Current research focuses on understanding the impact of different noise types and distributions on convergence rates, generalization performance, and privacy guarantees, often within the context of overparameterized models and specific algorithms like stochastic gradient descent (SGD) and its differentially private variants. This work is significant because it helps clarify the role of noise in mitigating overfitting, achieving privacy-preserving training, and improving the efficiency and robustness of optimization algorithms for complex machine learning tasks, including unlearning. Furthermore, recent studies are investigating the fundamental limitations of noisy gradient descent, exploring the relationship between initial network architecture and the learnability of target functions.