OpenTOS: Open-source System for Transfer Learning Bayesian Optimization

In recent years, many studies successfully integrated transfer learning techniques to improve the performance of Bayesian optimization. However, these advanced methods have not been widely adopted in real-world applications due to their inherent complexity and challenges in re-implementation and reproducibility. In this work, we introduce OpenTOS, an open-source system designed for transfer learning in Bayesian optimization. OpenTOS introduces a new implementation paradigm for these methods, allowing users to build different algorithms by choosing algorithmic components, similar to assembling LEGO blocks. Additionally, OpenTOS provides robust data management for supporting transfer learning with data from various sources. We also developed a web interface that allows for interactive building, analysis, and visualization of the optimization process. Powered by LLM, this interface offers a conversational experience, allowing users to interact with the system through natural language dialogue. OpenTOS is available as open-source on https://github.com/COLA-Laboratory/TransOPTGitHub .