Agent Skills
› brycewang-stanford/Awesome-Journal-Skills
› aistats-artifact-evaluation
aistats-artifact-evaluation
GitHub用于将AISTATS论文的代码、数据、证明及日志打包为匿名补充材料或公开工件。涵盖证据规划、去匿名化、复现地图构建，以及针对统计声明（如蒙特卡洛模拟）的标准化封装指南。
Trigger Scenarios
准备AISTATS论文的补充材料
需要匿名化处理代码和数据
封装蒙特卡洛研究脚本
生成可复现的实验环境说明
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aistats-artifact-evaluation -g -y
SKILL.md
Frontmatter
{
"name": "aistats-artifact-evaluation",
"description": "Use when packaging AISTATS code, data, proofs, simulation scripts, notebooks, random seeds, and logs as anonymous supplementary evidence or public post-acceptance artifacts, even when there is no separate artifact badge. Covers what statistically minded AISTATS reviewers inspect first and how to make Monte Carlo studies turnkey."
}
AISTATS Artifact Evaluation
Use this for evidence packaging around AISTATS. The venue centers on artificial intelligence, statistics, and machine learning, so artifacts should make statistical and computational claims inspectable.
Artifact plan
- Decide what evidence reviewers need: proof details, derivations, simulation scripts, benchmark code, datasets, preprocessing, hyperparameter sweeps, random seeds, logs, or qualitative examples.
- Keep decision-critical evidence in the main paper or appendix; optional run files can live in supplementary material.
- Anonymize repository history, paths, notebook metadata, license headers, organization names, cluster paths, grants, and commit authors.
- Include a minimal reproduction map: environment, dependencies, hardware, commands, expected outputs, runtime, seeds, and known nondeterminism.
- For restricted data, give enough provenance and processing detail for credible reproduction without violating data-use terms.
- After acceptance, replace anonymous archives with public, licensed, citable artifacts when feasible.
What AISTATS evidence reviewers open first
| Claim type | First artifact inspected | Common failure caught |
|---|---|---|
| Convergence rate or regret bound | Proof appendix and constants | Condition used in the proof but missing from the theorem statement |
| Monte Carlo simulation | Seeded simulation script | Plots cannot be regenerated because seeds and replication counts are absent |
| Benchmark comparison | Training and evaluation configs | Baseline tuning budget undocumented |
| Bayesian or MCMC method | Sampler diagnostics and chain logs | No convergence statistics or trace evidence anywhere |
Because AISTATS reviewers are often statisticians, they will rerun a small simulation far more readily than they will retrain a deep model, so make synthetic studies turnkey before polishing anything else.
Worked vignette: packaging a Monte Carlo study
A hypothetical submission proposes a doubly robust treatment-effect estimator with a root-n normality guarantee, validated on synthetic causal data plus two real benchmarks.
- Ship the data-generating process as one parameterized script rather than constants buried in notebooks, so reviewers can vary n, dimension, and confounding strength.
- Record the replication count and the exact seed sequence used for every coverage and bias table; AISTATS-style claims about interval coverage are meaningless without them.
- Emit tables directly from logged results so the PDF numbers and artifact numbers cannot drift apart.
- State explicitly where the simulated regime satisfies the theorem assumptions and where it deliberately violates them, since that mapping is what statistical reviewers grade.
Calibration anchors
- Supplementary inspection at AISTATS is at reviewer discretion; assume only the README and one entry script get opened, and design accordingly.
- Upload size limits and accepted formats vary by cycle; verify against the current OpenReview submission form rather than past years.
Output format
[Artifact role] anonymous supplement / camera-ready release / public archive
[Contents] <code/data/proofs/logs/notebooks>
[Anonymity risks] <paths/metadata/licenses/URLs>
[Reproduction level] turnkey / scripted / descriptive / weak
[Fixes before upload] <ordered list>
Version History
- 1839142 Current 2026-07-05 12:12
