Hyper Parameter Tuning

Hyper-parameter tuning (HPT) is the crucial process of finding optimal settings for a machine learning model's parameters that are not learned during training. Current research focuses on developing more efficient and robust HPT methods, including those based on Bayesian optimization, multi-armed bandits, evolutionary algorithms, and gradient-based approaches, often applied within frameworks like federated learning or for specific model architectures such as neural networks and reinforcement learning agents. These advancements aim to reduce the computational cost and time associated with HPT, improving the scalability and practicality of machine learning across diverse applications, from autonomous driving to natural language processing. The ultimate goal is to automate and optimize this critical step, leading to better model performance and more efficient use of computational resources.