Covariance-Adaptive Sequential Black-box Optimization for Diffusion Targeted Generation
<|reference_start|>Covariance-Adaptive Sequential Black-box Optimization for Diffusion Targeted Generation: Diffusion models have demonstrated great potential in generating high-quality content for images, natural language, protein domains, etc. However, how to perform user-preferred targeted generation via diffusion models with only black-box target scores of users remains challenging. To address this issue, we first formulate the fine-tuning of the targeted reserve-time stochastic differential equation (SDE) associated with a pre-trained diffusion model as a sequential black-box optimization problem. Furthermore, we propose a novel covariance-adaptive sequential optimization algorithm to optimize cumulative black-box scores under unknown transition dynamics. Theoretically, we prove a $O(\frac{d^2}{\sqrt{T}})$ convergence rate for cumulative convex functions without smooth and strongly convex assumptions. Empirically, experiments on both numerical test problems and target-guided 3D-molecule generation tasks show the superior performance of our method in achieving better target scores.<|reference_end|>
@article{lyu2024covariance-adaptive,
    title={Covariance-Adaptive Sequential Black-box Optimization for Diffusion
  Targeted Generation},
    author={Yueming Lyu, Kim Yong Tan, Yew Soon Ong, Ivor W. Tsang},
    journal={arXiv preprint arXiv:2406.00812},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.00812},
    primaryClass={stat.ML cs.LG}
}