Inversion-based Latent Bayesian Optimization

• URL: http://arxiv.org/abs/2411.05330v1
• Date: Fri, 08 Nov 2024 05:06:47 GMT
• Title: Inversion-based Latent Bayesian Optimization
• Authors: Jaewon Chu, Jinyoung Park, Seunghun Lee, Hyunwoo J. Kim,
• Abstract summary: Inversion-based Latent Bayesian Optimization (InvBO) is a plug-and-play module for Latent Bayesian optimization. InvBO consists of two components: an inversion method and a potential-aware trust region anchor selection. Experimental results demonstrate the effectiveness of InvBO on nine real-world benchmarks.
• Score: 18.306286370684205
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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