aistats-reproducibility
GitHub用于增强AISTATS论文的可重复性证据，涵盖官方清单、统计假设、证明、数据集、超参数、随机种子及不确定性估计。执行清单与声明一致性审计，确保代码/数据发布说明准确，避免 reviewer 因矛盾或遗漏标记为负面信号。
Trigger Scenarios
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aistats-reproducibility -g -y
SKILL.md
Frontmatter
{
"name": "aistats-reproducibility",
"description": "Use when strengthening AISTATS reproducibility evidence, including the official reproducibility checklist, statistical assumptions, proofs, datasets, hyperparameters, random seeds, compute, uncertainty estimates, baselines, code\/data release statements, and checklist-to-claim consistency audits."
}
AISTATS Reproducibility
Use this before submission and again before camera-ready. Reopen the current CFP and OpenReview forms to confirm whether a reproducibility checklist is required.
Evidence map
- Map each theorem, algorithmic claim, simulation claim, and empirical claim to a verifiable location in the paper, appendix, supplement, or artifact package.
- For theory, state assumptions, proof dependencies, convergence conditions, constants, and failure modes clearly enough for statistical readers.
- For experiments, report datasets, splits, preprocessing, evaluation metrics, baselines, hyperparameter ranges, final selected settings, seeds, repeated runs, compute, and runtime.
- For small performance differences, add uncertainty estimates: standard errors, confidence intervals, paired tests, bootstrap intervals, or repeated trials as appropriate.
- Explain missing code/data honestly and describe how a reader could reproduce the analysis in principle.
- Keep the checklist consistent with the manuscript; contradictions between checklist and paper are review-risk multipliers.
Checklist-to-claim audit table
| Checklist item | Pure-theory answer | Theory-plus-experiments answer |
|---|---|---|
| Code availability | NA only if there is literally no computation | Anonymous archive, or an honest stated reason |
| Assumptions stated | Every theorem lists its conditions inline | Plus a note on which experiments satisfy them |
| Error bars | NA for deterministic results | Required for every stochastic figure and table |
| Compute resources | NA | Hardware, runtime, and total number of runs |
Marking NA on an item the paper actually triggers is a recognizable AISTATS red flag, because reviewers cross-check checklist answers against the PDF and read contradictions as carelessness about the rest of the paper.
Vignette: a rates-plus-simulation paper
Consider a submission proving posterior contraction rates for a Bayesian nonparametric model, validated by MCMC simulation. Its reproducibility spine: prior hyperparameters and their selection rule, chain length, burn-in, convergence diagnostics, replication seeds, and a statement of which contraction-theorem conditions the simulated model satisfies — plus one honest sentence about the condition it does not.
Degrees of reproducibility
- Turnkey: one command regenerates each figure from logged seeds.
- Scripted: scripts exist but require documented manual steps or external data access.
- Descriptive: prose detailed enough that a competent reader could rebuild the pipeline.
For AISTATS, simulations should be turnkey because statistician reviewers actually rerun them; large real-data pipelines may stay scripted with deviations documented. Stating the achieved level honestly beats overpromising turnkey behavior that fails on a clean machine.
Output format
[Claim inventory] <claim -> evidence location>
[Checklist status] complete / inconsistent / missing
[Statistical reproducibility gaps] <assumptions/seeds/uncertainty/hyperparameters/compute>
[Paper fixes] <must appear in main PDF>
[Supplement fixes] <appendix or artifact additions>
Version History
- 1839142 Current 2026-07-05 12:12
