Latent Space Bayesian Optimization with Latent Data Augmentation for Enhanced Exploration

arXiv:2302.02399v4 Announce Type: replace
Abstract: 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 …

abstract arxiv augmentation autoencoders bayesian capabilities challenges cs.lg data exploration generate generative generative models however objects optimization space type vae variational autoencoders