{
  "abstract": "A Mixture-of-Experts (MoE) framework is proposed for quickest change detection (QCD) where there are multiple experts, and each expert produces a sequence of statistics for change detection. A change is declared when a weighted sum of these statistics crosses a threshold. This framework is then applied to the problem of multi-stream QCD to obtain a family of powerful algorithms that outperform the best known algorithms in the literature. In the multi-stream QCD problem, a decision-maker observes multiple streams of observations. At the change point, the distribution of an unknown subset of the streams changes. The optimal algorithm based on a generalized likelihood ratio is computationally expensive. One of the most computationally and performance-efficient algorithms in the literature is the rank-weighted sum (RWS) scheme based on Shiryaev-Roberts statistics. It is shown in the paper that the MoE-based QCD algorithm for multi-stream data outperforms the RWS scheme. This is demonstrated through simulation results on real-valued Gaussian mean shift data and high-dimensional data from Gaussian--Bernoulli Restricted Boltzmann Machines (GB-RBMs).",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.20876",
  "author_total": 4,
  "authors": [
    {
      "name": "Wuxia Chen"
    },
    {
      "name": "Arman Adibi"
    },
    {
      "name": "Taposh Banerjee"
    },
    {
      "name": "Vahid Tarokh"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/b15gy6pj3tesq.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.20876",
  "fulltext_url": null,
  "id": "b15gy6pj3tesq",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.20876",
  "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",
      "eess.SP"
    ],
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "arxiv",
      "stat.ML"
    ],
    [
      "openalex",
      "1804|Advanced Statistical Process Monitoring"
    ]
  ],
  "openalex": "W7222533387",
  "pdf_url": "https://arxiv.org/pdf/2610.20876",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-14",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/b15gy6pj3tesq.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:30:00Z",
      "native_id": "2610.20876",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.20876"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "A Mixture-of-Experts Framework for Multi-Stream Quickest Change Detection",
  "title_text": "A Mixture-of-Experts Framework for Multi-Stream Quickest Change Detection",
  "type": "preprint",
  "updated_at": "2026-10-30T12:30:00Z",
  "url": "https://research.draconitic.com/paper/b15gy6pj3tesq",
  "venue": "arXiv",
  "versions": []
}