{
  "abstract": "Dimensionality reduction is widely used to visualize high-dimensional data and guide scientific discovery, yet interpreting which original features contribute to low-dimensional representations remains challenging, particularly for black-box methods such as t-SNE and UMAP. We introduce \\textbf{MEDALLION} (Manifold Embedding Distillation and Attributions for Low-dimensional Latent InterpretatiON), a novel framework for interpreting low-dimensional embeddings through knowledge distillation and neural feature attribution. MEDALLION distills any given embedding with a neural encoder which we explain via a broad range of existing neural feature attribution methods. Through experiments on MNIST and applications in single-cell transcriptomics, we show MEDALLION provides informative feature attributions that cast additional insights into the underlying embedding and compare its performance with existing interpretability approaches.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.08767",
  "author_total": 4,
  "authors": [
    {
      "name": "Vicky Jung"
    },
    {
      "name": "Claire M. He"
    },
    {
      "name": "Irene Chang"
    },
    {
      "name": "Genevera I. Allen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/c67s0t4v12g20.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.08767",
  "fulltext_url": null,
  "id": "c67s0t4v12g20",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.08767",
  "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.LG"
    ],
    [
      "arxiv",
      "stat.ML"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.08767",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-05",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/c67s0t4v12g20.ris",
  "sources": [
    {
      "first_seen": "2026-11-10T06:21:13Z",
      "native_id": "2611.08767",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.08767"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ML",
      "subfield": "statistics"
    }
  ],
  "title": "MEDALLION: Manifold Embedding Distillation and Attributions for Low-dimensional Latent InterpretatiON",
  "title_text": "MEDALLION: Manifold Embedding Distillation and Attributions for Low-dimensional Latent InterpretatiON",
  "type": "preprint",
  "updated_at": "2026-11-10T06:21:13Z",
  "url": "https://research.draconitic.com/paper/c67s0t4v12g20",
  "venue": "arXiv",
  "versions": []
}