High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning

• DOI: 10.48550/arxiv.2106.03609
• Publication Date: 2021-01-01
• Authors: Antoine Grosnit; Rasul Tutunov; Alexandre Max Mar...; Ryan‐Rhys Griffiths; Alexander I. Cowe...; Lin Yang; Lin Zhu; Wenlong Lyu; Zhitang Chen; Jun Wang; Jan Peters; Haitham Bou Ammar
• Keywords: Benchmark (surveying); Bayesian Optimization

We introduce a method combining variational autoencoders (VAEs) and deep metric learning to perform Bayesian optimisation (BO) over high-dimensional structured input spaces. By adapting ideas from learning, we use label guidance the blackbox function structure VAE latent space, facilitating Gaussian process fit yielding improved BO performance. Importantly for problem settings, our operates in semi-supervised regimes where only few labelled data points are available. run experiments on three real-world tasks, achieving state-of-the-art results penalised logP molecule generation benchmark using just 3% of required by previous approaches. As theoretical contribution, present proof vanishing regret BO.