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An adaptive approach to Bayesian Optimization with switching costs
We investigate modifications to Bayesian Optimization for a resource-constrained setting of sequential experimental design where changes to certain design variables of the search space incur a switching cost.
A Principled Method for the Creation of Synthetic Multi-fidelity Data Sets
Multifidelity and multioutput optimisation algorithms are of active interest in many areas of computational design as they allow cheaper computational proxies to be used intelligently to aid experimental searches for high-performing species.
Powerful, transferable representations for molecules through intelligent task selection in deep multitask networks
Chemical representations derived from deep learning are emerging as a powerful tool in areas such as drug discovery and materials innovation.
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