Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow
This provides a modular toolkit for researchers and engineers to tackle custom optimization problems, but it is incremental as it builds on existing methods with a new implementation.
The authors introduced Trieste, an open-source Python package for Bayesian optimization and active learning that leverages TensorFlow for scalability and efficiency, enabling plug-and-play integration of models like Gaussian processes and neural networks.
We present Trieste, an open-source Python package for Bayesian optimization and active learning benefiting from the scalability and efficiency of TensorFlow. Our library enables the plug-and-play of popular TensorFlow-based models within sequential decision-making loops, e.g. Gaussian processes from GPflow or GPflux, or neural networks from Keras. This modular mindset is central to the package and extends to our acquisition functions and the internal dynamics of the decision-making loop, both of which can be tailored and extended by researchers or engineers when tackling custom use cases. Trieste is a research-friendly and production-ready toolkit backed by a comprehensive test suite, extensive documentation, and available at https://github.com/secondmind-labs/trieste.