{
  "abstract": "This paper asks whether group distributionally robust optimisation raises the worst-group macro-F1 of a block-masked multimodal logistic regression with availability indicators for multilingual fact-checking. Stochastic Mirror-Prox solves the resulting convex–concave saddle-point problem. The corpus pools 49,584 X-Fact and MediaEval records with 90 language labels. On the in-domain split, the robust solution lifts worst-group macro-F1 from 0.313 under pooled empirical risk minimisation to 0.401 (95% interval of the gain [0.025,0.134]). Overall macro-F1 drops by 0.040, and dispersion of balanced accuracy across languages falls from 0.166 to 0.131. The gain lies in X-Fact claim groups and the cost in MediaEval posts (macro-F1 0.887 to 0.809). Removing the availability indicators moves no worst-group or overall macro-F1 by more than 0.007. The worst-group difference is negative on unseen languages and not resolved on unseen sites or events. A Friedman test detects no difference between solvers in typical group performance. At equal numbers of sampled records, stochastic mirror descent reaches a smaller mean duality gap than Mirror-Prox at each measured mini-batch size from 2 to 128. On this instance, extrapolation does not repay its second call.",
  "abstract_shown": true,
  "affiliations": [
    "Faculty of Arts and Social Sciences, The University of Sydney, Sydney, NSW 2006, Australia",
    "School of Economics, Peking University, Beijing 100871, China",
    "China-ASEAN Institute of Statistics, Guangxi University of Finance and Economics, Nanning 530003, China",
    "Graduate School of Technology and Management, Kyung Hee University, Yongin-si 17104, Gyeonggi-do, Republic of Korea"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Shuyun Li"
    },
    {
      "affs": [
        1
      ],
      "name": "Fan Si",
      "orcid": "0009-0008-2764-6780"
    },
    {
      "affs": [
        2
      ],
      "name": "Dongxu Mo"
    },
    {
      "affs": [
        3
      ],
      "name": "Chao He"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3d75mn0be7q6x.bib",
  "cited_by": 0,
  "doi": "10.3390/math14193574",
  "fulltext_url": null,
  "id": "3d75mn0be7q6x",
  "issns": [
    "2227-7390"
  ],
  "landing_url": "https://www.mdpi.com/2227-7390/14/19/3574",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1702|Stochastic Gradient Optimization Techniques"
    ]
  ],
  "openalex": "W7216214909",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-02",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/3d75mn0be7q6x.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:29Z",
      "native_id": "10.3390/math14193574",
      "source": "crossref",
      "url": "https://www.mdpi.com/2227-7390/14/19/3574"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "issn:2227-7390",
      "subfield": "general"
    }
  ],
  "title": "Stochastic Mirror-Prox Optimisation of Group Distributionally Robust Multimodal Logistic Regression for Multilingual Fact-Checking Verdict Prediction",
  "title_text": "Stochastic Mirror-Prox Optimisation of Group Distributionally Robust Multimodal Logistic Regression for Multilingual Fact-Checking Verdict Prediction",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:29Z",
  "url": "https://research.draconitic.com/paper/3d75mn0be7q6x",
  "venue": "Mathematics",
  "versions": []
}