Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces


High-dimensional black-box optimisation remains an important yet notoriously challenging problem. Despite the success of Bayesian optimisation methods on continuous domains, domains that are categorical, or that mix continuous and categorical variables, remain challenging. We propose a novel solution – we combine local optimisation with a tailored kernel design, effectively handling high-dimensional categorical and mixed search spaces, whilst retaining sample efficiency. We further derive convergence guarantee for the proposed approach. Finally, we demonstrate empirically that our method outperforms the current baselines on a variety of synthetic and real-world tasks in terms of performance, computational costs, or both.

In 38th International Conference on Machine Learning, 2021
Cong Lu
Cong Lu
DPhil Student

My research interests span deep reinforcement learning, meta-learning and Bayesian Optimisation.