Newest papers

SABLE: Scalable Affine Bayesian Latent-space Exploration

Erik Bodin, Lisa Maria Kreußer, Donney Fan, Henry B. Moss, Geoff Pleiss, Nicolas Durrande · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.31495 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

诸如工程设计和蛋白质等复杂的效率预测对象往往难以优化,因为它们的设计空间显示高维结构,客观评估是昂贵的。生成模型提供连续的隐形呈现,但它们的隐形空间可以有数千到数万个尺寸,创建一个模式,其中隐形尺寸D$大大超过评估预算。我们引入了SABLE,一个高维隐形空间的贝雅斯式优化方法,具有同位素分布和有限的预算。SABLE使用一个贝雅斯式隐形替代剂来预测和收购,将较高的尺寸D$视为替代差异。在$N$观察后,倾斜更新尺寸最多为$N-1$,从而使每个查询分解成一个范围组成部分,以预期

Machine translation (Meta M2M100); the English original follows.

Abstract

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 prediction and acquisition, treating higher-order structure as surrogate discrepancy. After $N$ observations, the slope update has dimension at most $N-1$, yielding a decomposition of each query into an in-span component optimised by expected improvement (EI) in at most $N$ dimensions and an off-span component governed by a scalar norm. We show that the off-span norm implicitly selected by affine EI can lead to underexploration, and SABLE instead prescribes it using a finite-horizon schedule, without restricting search to a learned subspace. A recompression scheme reduces information to $\mathcal O(M)$ pseudo-observations, bounding refitting cost by $\mathcal O(M^2D+M^3)$ while keeping parameter inference, prediction, and acquisition decisions to tens of milliseconds. Across synthetic benchmarks and latent-space optimisation tasks, SABLE achieves high sample efficiency and reduces decision time by several orders of magnitude at large budgets.

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Topics: Bounding overwatch, Bayesian probability, Affine transformation, Dimension (graph theory), Computer science, Mathematics

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