logbook.json

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  "title": "Reproduction: High-Accuracy Sampling for Diffusion Models and Log-Concave Distributions",
  "emoji": "🎯",
  "space_id": "byte-vortex/repro-high-accuracy-sampling-for-diffusion-models-and-log-concave-distributions",
  "paper": {
    "arxiv_id": "2602.01338"
  },
  "tags": [
    "icml2026-repro",
    "paper-GW3umRqsZZ"
  ],
  "updated_at": "2026-07-31T15:45:17+00:00",
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    "slug": "index",
    "title": "Reproduction: High-Accuracy Sampling for Diffusion Models and Log-Concave Distributions",
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      {
        "slug": "executive-summary",
        "title": "Executive summary",
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      {
        "slug": "claim-1-the-paper-s-first-order-rejection-sampling-fors-meta-algorithm-theorem-3-1-produces-samples-with-error-using-sample-complexity-bounded-by-3be-2b-log-2-with-probability-1",
        "title": "Claim 1: The paper's First-Order Rejection Sampling (FORS) meta-algorithm (Theorem 3.1) produces samples with error δ using sample complexity bounded by 3Be^(2B)log(2/δ) with probability 1-δ.",
        "file": "pages/claim-1-the-paper-s-first-order-rejection-sampling-fors-meta-algorithm-theorem-3-1-produces-samples-with-error-using-sample-complexity-bounded-by-3be-2b-log-2-with-probability-1/page.md",
        "children": []
      },
      {
        "slug": "claim-2-under-only-a-finite-second-moment-assumption-minimal-assumptions-the-diffusion-sampler-achieves-query-complexity-o-d-log-1-log-1-giving-polylog-1-dependence-rather-than-the-poly-1-of-prior-work-theorem-4-1-section-4",
        "title": "Claim 2: Under only a finite second-moment assumption (minimal assumptions), the diffusion sampler achieves query complexity O(d·log²(1/δ) + log³(1/δ)), giving polylog(1/δ) dependence rather than the poly(1/δ) of prior work (Theorem 4.1, Section 4)",
        "file": "pages/claim-2-under-only-a-finite-second-moment-assumption-minimal-assumptions-the-diffusion-sampler-achieves-query-complexity-o-d-log-1-log-1-giving-polylog-1-dependence-rather-than-the-poly-1-of-prior-work-theorem-4-1-section-4/page.md",
        "children": []
      },
      {
        "slug": "claim-3-under-a-non-uniform-lipschitz-condition-on-the-score-assumption-4-3-a-ddpm-like-sampler-achieves-total-variation-error-controlled-via-chi-squared-divergence-with-complexity-o-dl-log-d-log-d-l-log-d-theorem-4-4",
        "title": "Claim 3: Under a non-uniform Lipschitz condition on the score (Assumption 4.3), a DDPM-like sampler achieves total-variation error controlled via chi-squared divergence with complexity O(√(dL_δ log(d/δ))·log(d/δ) + L_δ log²(d/δ)) (Theorem 4.4)",
        "file": "pages/claim-3-under-a-non-uniform-lipschitz-condition-on-the-score-assumption-4-3-a-ddpm-like-sampler-achieves-total-variation-error-controlled-via-chi-squared-divergence-with-complexity-o-dl-log-d-log-d-l-log-d-theorem-4-4/page.md",
        "children": []
      },
      {
        "slug": "claim-4-for-distributions-with-low-intrinsic-dimension-d-an-adaptive-step-size-method-attains-complexity-o-d-log-d-m-replacing-the-ambient-dimension-d-with-d-theorem-4-6",
        "title": "Claim 4: For distributions with low intrinsic dimension d★, an adaptive-step-size method attains complexity O(d★·log²((d+M₂²)/δ²)), replacing the ambient dimension d with d★ (Theorem 4.6)",
        "file": "pages/claim-4-for-distributions-with-low-intrinsic-dimension-d-an-adaptive-step-size-method-attains-complexity-o-d-log-d-m-replacing-the-ambient-dimension-d-with-d-theorem-4-6/page.md",
        "children": []
      },
      {
        "slug": "claim-5-section-5-extends-the-fors-framework-to-sample-from-general-log-concave-distributions-using-only-gradient-evaluations-no-density-evaluations-giving-the-first-polylog-1-sampler-in-this-setting-section-5",
        "title": "Claim 5: Section 5 extends the FORS framework to sample from general log-concave distributions using only gradient evaluations (no density evaluations), giving the first polylog(1/δ) sampler in this setting (Section 5)",
        "file": "pages/claim-5-section-5-extends-the-fors-framework-to-sample-from-general-log-concave-distributions-using-only-gradient-evaluations-no-density-evaluations-giving-the-first-polylog-1-sampler-in-this-setting-section-5/page.md",
        "children": []
      },
      {
        "slug": "conclusion",
        "title": "Conclusion",
        "file": "pages/conclusion/page.md",
        "children": []
      }
    ]
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