We develop Scalable Latent Exploration Score (ScaLES) to mitigate
over-exploration in Latent Space Optimization (LSO), a popular method for
solving black-box discrete optimization problems. LSO utilizes continuous
optimization within the latent space of a Variational Autoencoder (VAE) and is
known to be susceptible to over-exploration, which manifests in unrealistic
solutions that reduce its practicality. ScaLES is an exact and theoretically
motivated method leveraging the trained decoder's approximation of the data
distribution. ScaLES can be calculated with any existing decoder, e.g. from a
VAE, without additional training, architectural changes, or access to the
training data. Our evaluation across five LSO benchmark tasks and three VAE
architectures demonstrates that ScaLES enhances the quality of the solutions
while maintaining high objective values, leading to improvements over existing
solutions. We believe that new avenues to LSO will be opened by ScaLES ability
to identify out of distribution areas, differentiability, and computational
tractability. Open source code for ScaLES is available at
this https URL

我们开发了可扩展的潜在探索评分（ScaLES）来减轻潜在空间优化中的过度探索问题，通过利用训练解码器对数据分布的近似来进行计算，ScaLES 提高了解决方案的质量，同时保持较高的客观值，从而改进了现有的解决方案。