Adaptive Stepsizes

Adaptive stepsizes in optimization algorithms aim to automatically adjust learning rates during training, eliminating the need for manual tuning and potentially improving convergence speed and generalization performance. Current research focuses on developing novel algorithms, such as tuning-free bilevel optimization methods and distributed adaptive minimax approaches, that achieve theoretical convergence guarantees while adapting stepsizes effectively across various architectures, including deep neural networks (DNNs) and transformers. These advancements are significant because they enhance the robustness and efficiency of training complex models, leading to improved performance in diverse machine learning applications. Furthermore, investigations into the relationship between stepsize scheduling (e.g., cyclic or randomized) and model generalization are yielding insights into optimal training strategies.