Latent Bayesian optimization (LBO) approaches have successfully adopted Bayesian optimization over a continuous latent space by employing an encoder-decoder architecture to address the challenge of optimization in a high dimensional or discrete input space. LBO learns a surrogate model to approximate the black-box objective function in the latent space. However, we observed that most LBO methods suffer from the `misalignment problem`, which is induced by the reconstruction error of the encoder-decoder architecture. It hinders learning an accurate surrogate model and generating high-quality solutions. In addition, several trust region-based LBO methods select the anchor, the center of the trust region, based solely on the objective function value without considering the trust region`s potential to enhance the optimization process. To address these issues, we propose Inversion-based Latent Bayesian Optimization (InvBO), a plug-and-play module for LBO. InvBO consists of two components: an inversion method and a potential-aware trust region anchor selection. The inversion method searches the latent code that completely reconstructs the given target data. The potential-aware trust region anchor selection considers the potential capability of the trust region for better local optimization. Experimental results demonstrate the effectiveness of InvBO on nine real-world benchmarks, such as molecule design and arithmetic expression fitting tasks. Code is available at https://github.com/mlvlab/InvBO.

翻译：潜在贝叶斯优化方法通过采用编码器-解码器架构，成功地在连续潜在空间上实施贝叶斯优化，以应对高维或离散输入空间中的优化挑战。该方法学习一个代理模型来近似潜在空间中的黑盒目标函数。然而，我们观察到大多数潜在贝叶斯优化方法存在“错位问题”，该问题由编码器-解码器架构的重构误差所引发。这阻碍了学习精确的代理模型并生成高质量的解。此外，几种基于信任区域的潜在贝叶斯优化方法在选择信任区域中心（锚点）时，仅依据目标函数值，而未考虑信任区域在提升优化过程方面的潜力。为解决这些问题，我们提出了基于反转的潜在贝叶斯优化，这是一个即插即用的潜在贝叶斯优化模块。该模块包含两个组件：一种反转方法和一种潜力感知的信任区域锚点选择策略。反转方法搜索能够完全重构给定目标数据的潜在编码。潜力感知的信任区域锚点选择则考虑了信任区域在实现更好局部优化方面的潜在能力。实验结果表明，该方法在九个真实世界基准测试（如分子设计和算术表达式拟合任务）上具有有效性。代码可在 https://github.com/mlvlab/InvBO 获取。