BOTORCH: A Framework for Efficient Monte-Carlo Bayesian Optimization

 BOTORCH: A Framework for Efficient Monte-Carlo Bayesian Optimization

To address these problems, we developed BoTorch, a framework for Bayesian optimization research, and Ax, a robust platform for adaptive experimentation. BoTorch follows the same modular design philosophy as PyTorch, which makes it very easy for users to swap out or rearrange individual components in order to customize all aspects of their algorithm, thereby empowering researchers to do state-of-the art research on modern Bayesian optimization methods. By exploiting modern parallel computing paradigms on both CPUs and GPUs, it is also fast.

Core Data Science researchers discuss research award opportunity in adaptive experimentation