Writing study: Filtered Conformal Ellipsoids for Graph-Native Time Series
← NeurIPS 2026 oral writing corpus
Yannick Limmer · Official NeurIPS 2026 oral record
Figure, table and section ledger
Editorial contribution type: Theory & formal analysis (primary).
Layout extraction, not a required paper template. Counts are distinct caption labels; a multi-panel figure counts once. Section spans are estimated from heading positions, including floats and whitespace. Method and limitations.
- Canonical PDF
- 45 physical pages
- Before references / appendix
- ≈ 10.33 page-equivalents
- Main figures / tables
- 2 / 3
- Attached appendix figures / tables
- 1 / 22
- Attached appendix span
- ≈ 32.91 page-equivalents
Source-bound correction: Top-level heading and reference/appendix boundaries checked against source PDF text. Bibliography titles/author initials, numbered contribution/property bullets, diagram steps, equations and TOC entries are not substantive headings. Section spans are page-coordinate estimates, not ink area or prose-only length. This is an author manuscript mirror, not a verified accepted-version template. Source layout uses physical single-column y/page-height, including equation-heavy pages.
Section-by-section lengths and source pages
The title/abstract/front matter precede the first numbered heading. A span ends at the next detected top-level heading, not at the last sentence; end-page whitespace is included. References and statements are listed separately. These are not measurements of text-only or artwork area.
| Top-level heading | Starts on PDF page | Estimated span | Share of entire PDF |
|---|---|---|---|
| 1 Introduction | 1 | ≈ 1.37 pp. | 3.04% |
| 2 Related work | 3 | ≈ 0.39 pp. | 0.87% |
| 3 Preliminaries | 3 | ≈ 1.31 pp. | 2.91% |
| 4 Filtered conformal prediction | 4 | ≈ 0.83 pp. | 1.84% |
| 5 Theory | 5 | ≈ 2.21 pp. | 4.91% |
| 6 Experiments | 7 | ≈ 2.36 pp. | 5.24% |
| 7 Discussion and limitations | 10 | ≈ 0.9 pp. | 2% |
| Acknowledgments and Disclosure of Funding | 11 | ≈ 0.23 pp. | 0.51% |
| References | 11 | ≈ 1.77 pp. | 3.93% |
| A Observable-contraction theory | 13 | ≈ 3.61 pp. | 8.02% |
| B Full proofs and derivations | 16 | ≈ 7.73 pp. | 17.18% |
| C Filter specifications, training, and pseudocode | 24 | ≈ 4.15 pp. | 9.22% |
| D Baseline implementations | 28 | ≈ 2.86 pp. | 6.36% |
| E Additional experiments | 31 | ≈ 13.86 pp. | 30.8% |
| F Reproducibility | 45 | ≈ 0.7 pp. | 1.56% |
All detected figure and table locations
Attached appendix
- Table 4 · p. 26
- Table 5 · p. 29
- Table 6 · p. 30
- Table 7 · p. 31
- Table 8 · p. 32
- Table 9 · p. 35
- Table 10 · p. 35
- Figure 3 · p. 36
- Table 11 · p. 36
- Table 12 · p. 37
- Table 13 · p. 37
- Table 14 · p. 38
- Table 15 · p. 39
- Table 16 · p. 40
- Table 17 · p. 40
- Table 18 · p. 41
- Table 19 · p. 41
- Table 20 · p. 42
- Table 21 · p. 42
- Table 22 · p. 43
- Table 23 · p. 44
- Table 24 · p. 44
- Table 25 · p. 45
Argument and organization
- Problem and contribution
Separates learning an ellipsoid's shape from conformal calibration of its radius, avoiding reliance on Gaussian tail probabilities.
- Argument sequence
Presents construction, conditional theory and three numbered experimental questions before limitations.
- Evidence and verification
The headline table reports coverage next to width, defines the target tolerance and notes native versus shared mean predictors.
Problem, claim and contribution
Separates learning an ellipsoid's shape from conformal calibration of its radius, avoiding reliance on Gaussian tail probabilities.
Introduction; Figure 1 · Main paper
Argument and section order
Presents construction, conditional theory and three numbered experimental questions before limitations.
Sections 4–7 · Main paper
Figures, captions and visual explanation
The five-stage schematic distinguishes frozen filtering, scalar scoring and calibration. Coverage–width panels place a target line beside sharpness.
Figure 1, p. 2; Figure 2, p. 8 · Main paper
Tables, comparisons and evidence
The headline table reports coverage next to width, defines the target tolerance and notes native versus shared mean predictors.
Table 2, p. 8 · Main paper
Appendix and reproducibility
Proofs, algorithms, dataset taxonomy, mean-predictor ledger, per-seed results and assumption diagnostics have explicit sections and tables.
Appendices A–E · Main paper
Clarity and writing conventions
The text repeatedly distinguishes finite-trajectory diagnostics from certification of population assumptions, and retains full-graph non-winning cases.
Table 1; Section 7; Appendix E.15 · Main paper
What to reuse—and what not to infer
Transferable lesson: Place the comparison contract beside the numbers and distinguish empirical checks from proof assumptions.
Boundary: This is not universal full-graph leadership or conditional coverage. The source reports backbone- and scale-dependent limits.
Apply this lesson using the argument and evidence worksheet.
Inspection record and sources
- Checked
- 2026-10-02
- Reading status
- Writing-focused manuscript scan + selected visuals inspected
- Main text
- Writing-oriented distributed manuscript scan of Sections 1–7, pp. 1–10; references pp. 11–12: abstract/framing, section sequence, substantive text windows, captions, evidence organization and conclusion; not exhaustive technical verification.
- Appendix / supplement
- Writing-oriented scan of Appendices A–F, pp. 13–45; proof structure and experiment-ledger scan, not theorem re-derivation; code and experiments were not executed.
- Visually inspected PDF pages
- 2, 8
This is a writing-focused scan of the manuscript and the stated supplementary scope: framing, organization, visual explanation, evidence presentation and wording. It is not a line-by-line proof audit, reproduction of results or exhaustive scientific review.