Reformulates constrained black-box optimization as posterior inference in latent space of flow-based models amortized by outsourced diffusion models, claiming superior performance on synthetic and real tasks.
Classifier-free diffusion guidance
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Primal-dual guided decoding casts constrained discrete diffusion as a KL-regularized optimization solved online with adaptive Lagrangian multipliers to satisfy constraints while staying close to the unconstrained model distribution.
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Posterior Inference in Latent Space for Scalable Constrained Black-box Optimization
Reformulates constrained black-box optimization as posterior inference in latent space of flow-based models amortized by outsourced diffusion models, claiming superior performance on synthetic and real tasks.
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Primal-Dual Guided Decoding for Constrained Discrete Diffusion
Primal-dual guided decoding casts constrained discrete diffusion as a KL-regularized optimization solved online with adaptive Lagrangian multipliers to satisfy constraints while staying close to the unconstrained model distribution.