Latent Space Bayesian Optimization with Latent Data Augmentation for Enhanced Exploration
This work addresses a specific bottleneck in LSBO for researchers in generative modeling and optimization, offering incremental improvements through novel acquisition functions and VAE modifications.
The paper tackles the challenge of poor exploration in Latent Space Bayesian Optimization (LSBO) due to mismatches between generative models and optimization objectives, proposing a method that improves sample efficiency and exploration in tasks like image generation and chemical design.
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.