Acquisition Function-Guided Generation for Bayesian Optimization over Discrete Spaces
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The paper introduces a new Bayesian optimization approach tailored for generative models used in de novo discovery pipelines. By employing a linear surrogate model constrained to a spherical domain—where high‑dimensional latent vectors naturally concentrate—the authors derive nearly closed‑form solutions for both surrogate modeling and acquisition, achieving at least a 100‑fold speedup over existing methods. This acceleration enables Bayesian optimization to be used as a practical drop‑in component in pipelines that previously found it too slow to consider.
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