{
  "abstract": "In this work, we propose a transfer learning algorithm for covariate shift, integrating sample reweighting with representation learning. A key feature of our approach is the use of a lower-dimensional latent space for modeling, in contrast to conventional methods that operate directly in the high-dimensional ambient space. Our method estimates the density ratio of learned latent features for downstream weighted prediction. We establish explicit target-domain excess-risk rates with independent data folds for strictly sufficient learned representations with regular learned-space prediction scores and density ratios. We give dimension comparisons under stated smoothness conditions and a variance comparison for true weights and a fixed predictor. General finite-sample inequalities retain representation-quality errors. Experiments on simulated and real-world datasets demonstrate gains from combining learned representations and latent weighting, and reveal sensitivity to initialization and domain choice.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.15571",
  "author_total": 5,
  "authors": [
    {
      "name": "Yuling Jiao"
    },
    {
      "name": "Lican Kang"
    },
    {
      "name": "Yang Xiang"
    },
    {
      "name": "Jerry Zhijian Yang"
    },
    {
      "name": "Fusheng Zhou"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6fp2tsvcjbf37.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.15571",
  "fulltext_url": null,
  "id": "6fp2tsvcjbf37",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.15571",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "stat.ML"
    ],
    [
      "openalex",
      "1702|Domain Adaptation and Few-Shot Learning"
    ]
  ],
  "openalex": "W7221746851",
  "pdf_url": "https://arxiv.org/pdf/2610.15571",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-23",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6fp2tsvcjbf37.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:25Z",
      "native_id": "2610.15571",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.15571"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ML",
      "subfield": "statistics"
    }
  ],
  "title": "Latent-Space Importance Weighting under Covariate Shift",
  "title_text": "Latent-Space Importance Weighting under Covariate Shift",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:25Z",
  "url": "https://research.draconitic.com/paper/6fp2tsvcjbf37",
  "venue": "arXiv",
  "versions": []
}