Asynchronous Stochastic

Asynchronous stochastic methods address the challenges of optimizing systems where updates are made using noisy, delayed, or irregularly arriving data. Current research focuses on improving the convergence rates and robustness of algorithms like asynchronous stochastic gradient descent (SGD) and its variants, often employing techniques like mini-batching and adaptive learning rates to handle variable delays and stragglers. These advancements are significant for large-scale distributed and federated learning, reinforcement learning in complex environments, and Bayesian optimization of expensive experiments, enabling faster and more efficient optimization in diverse applications. The development of more stable and efficient asynchronous algorithms is crucial for handling the inherent uncertainties and delays present in many real-world systems.