{
  "abstract": "Riemannian stochastic gradient paths estimate constrained parameters, but confidence regions usually require estimating the Hessian and gradient-noise covariance. We develop self-normalized inference from one path by expressing retained iterates in a common random tangent space. Their average and centered partial sums share an unknown covariance factor that cancels in a Brownian bridge pivot. Under suitable regularity conditions, a joint expansion of the averaged estimator and integrated path controls nonlinear coordinate errors using conditional second moments. The procedure uses fixed-dimensional memory and extends to nondegenerate smooth functionals and alternative local coordinates. Numerical studies show competitive coverage relative to plug-in Wald inference while avoiding Hessian and residual-density estimation.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.01035",
  "author_total": 3,
  "authors": [
    {
      "name": "Xiaotian Chang"
    },
    {
      "name": "Qirui Hu"
    },
    {
      "name": "Yi Liu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2bxzzhgv3dq4k.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.01035",
  "fulltext_url": null,
  "id": "2bxzzhgv3dq4k",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.01035",
  "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/2611.01035",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/2bxzzhgv3dq4k.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T12:21:21Z",
      "native_id": "2611.01035",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.01035"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ML",
      "subfield": "statistics"
    }
  ],
  "title": "Self-Normalized Inference from Riemannian Stochastic Gradient Paths",
  "title_text": "Self-Normalized Inference from Riemannian Stochastic Gradient Paths",
  "type": "preprint",
  "updated_at": "2026-11-05T15:20:35Z",
  "url": "https://research.draconitic.com/paper/2bxzzhgv3dq4k",
  "venue": "arXiv",
  "versions": []
}