Acquisition Function-Guided Generation for Bayesian Optimization over Discrete Spaces

Key points

• In this work, we instead combine a pretrained generative model with a surrogate defined directly in data space, where inductive biases such as domain-specific kernels can be readily incorporated.1
• Rather than maximizing the acquisition function over the discrete design space, our method samples candidates from the generative model under a distribution tilted by the acquisition function.1
• To recover useful gradients for sampling, we embed this tilted distribution into a diffusion bridge that yields a smooth, posterior-averaged acquisition function.1
• This leads to acquisition function guided generation (AFGG), which we instantiate with a categorical diffusion model and an autoregressive normalizing flow.1

Sources (1)

• [1]Acquisition Function-Guided Generation for Bayesian Optimization over Discrete Spaces
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 12:07 AM

  Bayesian optimization (BO) over discrete spaces is challenging due to missing gradients and the sparsity of valid solutions.

  In this work, we instead combine a pretrained generative model with a surrogate defined directly in data space, where inductive biases such as domain-specific kernels can be readily incorporated.

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