4 papers
Meta-learning for sample-efficient Bayesian optimisation of fed-batch processes
Becky Langdon, Gabriel D. Patrón, Chrysoula D. Kappatou +6
The optimisation of fed-batch (bio)chemical process recipes is subject to inherent, underlying, and unmeasurable fluctuations across batches, whose trajectories are difficult to mo…
BoGrape: Bayesian optimization over graphs with shortest-path encoded
Yilin Xie, Shiqiang Zhang, Jixiang Qing +2
Graph-structured data are central to many scientific and industrial applications where the goal is to optimize expensive black-box objectives defined over graph structures or node…
The Catechol Benchmark: Time-series Solvent Selection Data for Few-shot Machine Learning
Toby Boyne, Juan S. Campos, Becky D. Langdon +11
Machine learning has promised to change the landscape of laboratory chemistry, with impressive results in molecular property prediction and reaction retro-synthesis. However, chemi…
Global optimization of graph acquisition functions for neural architecture search
Yilin Xie, Shiqiang Zhang, Jixiang Qing +2
Graph Bayesian optimization (BO) has shown potential as a powerful and data-efficient tool for neural architecture search (NAS). Most existing graph BO works focus on developing gr…