Hyper Parameter Optimization

Hyper-parameter optimization (HPO) aims to efficiently find the best settings for a machine learning model's parameters, maximizing performance and minimizing computational cost. Current research focuses on developing more efficient algorithms, including Bayesian optimization, reinforcement learning, and evolutionary methods, often integrated with novel architectures like transformers. These advancements are crucial for improving the scalability and performance of machine learning across diverse applications, from large language models to resource-constrained IoT devices, and are driving the development of automated machine learning tools.