Hp Optimization

Hyperparameter optimization (HPO) aims to efficiently find the best settings for machine learning models, significantly impacting their performance. Current research focuses on accelerating HPO, particularly for computationally expensive deep learning models, through techniques like multi-fidelity optimization and the development of faster benchmarking tools using zero-cost proxies. Prominent algorithms include Bayesian optimization and evolutionary strategies, applied to both low and high-dimensional hyperparameter spaces. Efficient HPO is crucial for advancing machine learning applications across diverse fields, from forecasting to high-dimensional regression problems, by enabling faster model development and improved accuracy.