Poster
in
Workshop: Bayesian Decision-making and Uncertainty: from probabilistic and spatiotemporal modeling to sequential experiment design
Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow
Henry Moss · Victor Picheny · Hrvoje Stojic · Sebastian Ober · Artem Artemev · Andrei Paleyes · Sattar Vakili · Stratis Markou · Jixiang Qing · Nasrulloh Loka · Ivo Couckuyt
Keywords: [ tensorflow ] [ Gaussian process ] [ Active Learning ] [ Bayesian Optimisation ] [ software ]
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.