Newest papers

SABLE: Scalable Affine Bayesian Latent-space Exploration

Erik Bodin, Lisa Maria Kreusser, Donney Fan, Henry Moss, Geoff Pleiss, Nicolas Durrande · 2026 · arXiv

DOI: 10.48550/arXiv.2610.31495 · open-access PDF

Japanese

エンジニアリングデザインやタンパク質などの複雑なオブジェクトは、設計スペースが高次元構造を示し、客観的評価が高価であるため、しばしば最適化することは困難である。生成型モデルは継続的な隠れた表示を提供するが、その隠れたスペースは数千から数万の次元を有することができ、隠れた次元$D$が評価予算を大幅に上回る体制を作成する。

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

Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange