Pretrained Optimization Model Enhances Zero-Shot Black Box Optimization
A new Pretrained Optimization Model (POM) has been developed by researchers to enhance zero-shot optimization, which involves an optimizer addressing a task that was not part of its training. This model utilizes insights from various optimization tasks to deliver effective solutions, either directly or by fine-tuning with limited samples. Tested against the BBOB benchmark and two robotic control tasks, POM surpasses leading black-box optimization techniques, especially in high-dimensional contexts. Notably, fine-tuning with a few samples and budget leads to considerable performance improvements. The study, published on arXiv (2405.03728), tackles the issue of hyperparameter tuning that many current optimizers face when dealing with new tasks, striving for dependable performance across different applications.
Key facts
- POM is a pretrained optimization model for zero-shot black-box optimization.
- It leverages knowledge from diverse tasks to optimize unseen tasks.
- Evaluated on BBOB benchmark and two robot control tasks.
- Outperforms state-of-the-art methods, especially for high-dimensional tasks.
- Fine-tuning with few-shot samples and budget improves performance significantly.
- Paper available on arXiv with ID 2405.03728.
- Addresses the need for intricate hyperparameter tuning in current optimizers.
- Aims for reliable and robust performance in various applications.
Entities
Institutions
- arXiv