Robust Guided Diffusion for Offline Black-Box Optimization
<|reference_start|>Robust Guided Diffusion for Offline Black-Box Optimization: Offline black-box optimization aims to maximize a black-box function using an offline dataset of designs and their measured properties. Two main approaches have emerged: the forward approach, which learns a mapping from input to its value, thereby acting as a proxy to guide optimization, and the inverse approach, which learns a mapping from value to input for conditional generation. (a) Although proxy-free~(classifier-free) diffusion shows promise in robustly modeling the inverse mapping, it lacks explicit guidance from proxies, essential for generating high-performance samples beyond the training distribution. Therefore, we propose \textit{proxy-enhanced sampling} which utilizes the explicit guidance from a trained proxy to bolster proxy-free diffusion with enhanced sampling control. (b) Yet, the trained proxy is susceptible to out-of-distribution issues. To address this, we devise the module \textit{diffusion-based proxy refinement}, which seamlessly integrates insights from proxy-free diffusion back into the proxy for refinement. To sum up, we propose \textit{\textbf{R}obust \textbf{G}uided \textbf{D}iffusion for Offline Black-box Optimization}~(\textbf{RGD}), combining the advantages of proxy~(explicit guidance) and proxy-free diffusion~(robustness) for effective conditional generation. RGD achieves state-of-the-art results on various design-bench tasks, underscoring its efficacy. Our code is at https://anonymous.4open.science/r/RGD-27A5/README.md.<|reference_end|>
@article{can2024robust,
    title={Robust Guided Diffusion for Offline Black-Box Optimization},
    author={Can (Sam) Chen, Christopher Beckham, Zixuan Liu, Xue Liu, Christopher
  Pal},
    journal={arXiv preprint arXiv:2410.00983},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.00983},
    primaryClass={cs.LG cs.AI}
}