{
  "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.31495",
  "author_total": 6,
  "authors": [
    {
      "name": "Erik Bodin"
    },
    {
      "name": "Lisa Maria Kreusser"
    },
    {
      "name": "Donney Fan"
    },
    {
      "name": "Henry Moss"
    },
    {
      "name": "Geoff Pleiss"
    },
    {
      "name": "Nicolas Durrande"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/05mnjexpabpet.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.31495",
  "fulltext_url": null,
  "id": "05mnjexpabpet",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.31495",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.AI"
    ],
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "stat.ML"
    ],
    [
      "openalex",
      "1803|Advanced Bandit Algorithms Research"
    ]
  ],
  "openalex": "W7223386280",
  "pdf_url": "https://arxiv.org/pdf/2610.31495",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-23",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/05mnjexpabpet.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:25Z",
      "native_id": "2610.31495",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.31495"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ML",
      "subfield": "statistics"
    }
  ],
  "title": "SABLE: Scalable Affine Bayesian Latent-space Exploration",
  "title_text": "SABLE: Scalable Affine Bayesian Latent-space Exploration",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:25Z",
  "url": "https://research.draconitic.com/paper/05mnjexpabpet",
  "venue": "arXiv",
  "versions": []
}