Erik Bodin, Lisa Maria Kreusser, Donney Fan, Henry Moss, Geoff Pleiss, Nicolas Durrande · 2026 · arXiv
DOI: 10.48550/arXiv.2610.31495 · open-access PDF
엔지니어링 디자인과 단백질과 같은 복잡한 개체는 설계 공간이 고차원 구조를 보여주고 객관적 평가가 비싸기 때문에 종종 최적화하기가 어렵습니다. 생성 모델은 지속적인 숨겨진 표현을 제공하지만 그 숨겨진 공간은 수천에서 수십만 개의 차원을 가질 수 있으며, 숨겨진 차원 $D$이 평가 예산을 크게 초과하는 체제를 만듭니다.
Machine translation (Meta M2M100); the English original follows.
Complex objects such as engineering designs and proteins are often difficult to optimise because their design spaces exhibit high-dimensional structure and objective evaluations are expensive. Generative models provide continuous latent representations, but their latent spaces can have thousands to tens of thousands of dimensions, creating a regime where the latent dimension $D$ greatly exceeds the evaluation budget. We introduce SABLE, a Bayesian optimisation method for high-dimensional latent spaces with an isotropic distribution and limited budget. SABLE uses a Bayesian affine surrogate for
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