Black-box optimization problems are ubiquitous and of importance in many critical areas of science and engineering. Bayesian optimisation (BO) over the past years has emerged as one of the most successful techniques for optimising expensive black-box objectives. However, efficient scaling of BO to high-dimensional settings has proven to be extremely challenging. Traditional strategies based on projecting high-dimensional input data to a lower-dimensional manifold, such as Variational autoencoders (VAE) and Generative adversarial networks (GAN) have improved BO performance in high-dimensional regimes, but their dependence on excessive labeled input data has been widely reported. In this work, we target the data-greedy nature of deep generative models by constructing uncertainty-aware task-specific labeled data augmentations using Gaussian processes (GPs). Our approach outperforms existing state-of-the-art methods on machine learning tasks and demonstrates more informative data representation with limited supervision.