Hyperparameter Space

Hyperparameter optimization (HPO) focuses on efficiently finding the best settings for a machine learning model's parameters, significantly impacting its performance. Current research emphasizes developing more efficient algorithms for navigating the often vast and complex hyperparameter space, including Bayesian optimization, evolutionary strategies, and bilevel approaches, and applying these to diverse models like diffusion models, variational autoencoders, and large language models. Effective HPO is crucial for improving the accuracy, training speed, and fairness of machine learning models across various applications, from medical image generation to large-scale online recommendations.