  Latent Space Bayesian Optimization (LSBO) combines generative models,
typically Variational Autoencoders (VAE), with Bayesian Optimization (BO) to
generate de-novo objects of interest. However, LSBO faces challenges due to the
mismatch between the objectives of BO and VAE, resulting in poor exploration
capabilities. In this paper, we propose novel contributions to enhance LSBO
efficiency and overcome this challenge. We first introduce the concept of
latent consistency/inconsistency as a crucial problem in LSBO, arising from the
VAE-BO mismatch. To address this, we propose the Latent Consistent
Aware-Acquisition Function (LCA-AF) that leverages consistent points in LSBO.
Additionally, we present LCA-VAE, a novel VAE method that creates a latent
space with increased consistent points through data augmentation in latent
space and penalization of latent inconsistencies. Combining LCA-VAE and LCA-AF,
we develop LCA-LSBO. Our approach achieves high sample-efficiency and effective
exploration, emphasizing the significance of addressing latent consistency
through the novel incorporation of data augmentation in latent space within
LCA-VAE in LSBO. We showcase the performance of our proposal via de-novo image
generation and de-novo chemical design tasks.
