{
  "abstract": "We study the problem of sampling from $μ(\\mathrm{d}x)\\propto e^{-V(x)}\\,\\mathrm{d}x$ on $\\mathbb{R}^d$, where $V$ is $α$-strongly convex and $β$-smooth, and write $κ:=β/α$. We design and analyze the Proximal Bouncy Particle Sampler (Proximal BPS), a new sampler that combines ideas from the proximal sampler and the bouncy particle sampler. From a warm start initialization with $ O(1) $ Rényi divergence w.r.t. $μ$, Proximal BPS returns a sample whose law is $\\varepsilon$-close to $μ$ in total variation distance using $\\widetilde O(\\sqrtκ\\,d^{1/4} \\,\\mathrm{polylog}(1/\\varepsilon))$ gradient queries in expectation.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.06905",
  "author_total": 4,
  "authors": [
    {
      "name": "Fan Chen"
    },
    {
      "name": "Sinho Chewi"
    },
    {
      "name": "Jianfeng Lu"
    },
    {
      "name": "Matthew S Zhang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/919aedb7yvkh9.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.06905",
  "fulltext_url": null,
  "id": "919aedb7yvkh9",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.06905",
  "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.DS"
    ],
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "math.PR"
    ],
    [
      "arxiv",
      "math.ST"
    ],
    [
      "arxiv",
      "stat.ML"
    ],
    [
      "arxiv",
      "stat.TH"
    ],
    [
      "openalex",
      "2613|Markov Chains and Monte Carlo Methods"
    ]
  ],
  "openalex": "W7211993388",
  "pdf_url": "https://arxiv.org/pdf/2609.06905",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-07",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/919aedb7yvkh9.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:58Z",
      "native_id": "2609.06905",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.06905"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.PR",
      "subfield": "probability"
    },
    {
      "field": "mathematics",
      "origin": "arxiv:math.ST",
      "subfield": "statistics"
    }
  ],
  "title": "Accelerated High-Accuracy Sampling from a Warm Start via the Proximal Bouncy Particle Sampler",
  "title_text": "Accelerated High-Accuracy Sampling from a Warm Start via the Proximal Bouncy Particle Sampler",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:07Z",
  "url": "https://research.draconitic.com/paper/919aedb7yvkh9",
  "venue": "arXiv",
  "versions": []
}